WP44: Part VIII category-level knowledge flows, actors and inequality (8 tables)

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 1 h ago (Nov 4, 2026) parent f3ac605 Browse files

52 changed files +770 −211

modified manuscript/appendices/appF_supplementary_tables.tex +0 −14

				`@@ -25,20 +25,6 @@ This appendix collects the generated tables not shown in the main text. Every ta`
25	\clearpage	25	\clearpage
26	\input{tables/tab_known_set}	26	\input{tables/tab_known_set}
27	\clearpage	27	\clearpage
28	−\input{tables/tab_ai_growth}
29	−\clearpage
30	−\input{tables/tab_ai_counts}
31	−\clearpage
32	−\input{tables/tab_ai_breaks}
33	−\clearpage
34	−\input{tables/tab_ai_altdates}
35	−\clearpage
36	−\input{tables/tab_ai_flows}
37	−\clearpage
38	−\input{tables/tab_ai_landmark_flags}
39	−\clearpage
40	−\input{tables/tab_ai_contribution_types}
41	−\clearpage
42	\input{tables/tab_ai_landmarks}	28	\input{tables/tab_ai_landmarks}
43	\clearpage	29	\clearpage
44	\input{tables/tab_security_counts}	30	\input{tables/tab_security_counts}

modified manuscript/sections/part05_ai.tex +570 −1

++% ============================================================================
++% Part V — The Artificial Intelligence Transformation
++% Written by Agent E (AI Research Specialist), 2026-11-03, from
++% literature_synthesis/agent_E/{review.md, claims.csv, contributions.csv,
++% open_problems.csv}. Evidence rules: landmark papers are known from metadata
++% and abstracts only (never full text); preprints are named as preprints; corpus
++% numbers enter only through the macros of tables/numbers.tex or the generated
++% tables tab_ai_*.tex (tables/make_tables.py, tables_part05). Claim identifiers
++% [E-nn] in comments refer to claims.csv. No causal claim about ChatGPT.
++% ============================================================================
++
++\section{Scope, evidence base and reading rules}
++\label{sec:ai-scope}
++
++This part synthesises the nine categories of the taxonomy that the ontology
++groups under \emph{AI and machine learning}: large language models, foundation
++models, multimodal AI, generative AI, AI agents and autonomous systems,
++reinforcement learning, machine-learning theory, deep-learning architectures,
++and AI safety and alignment. Three kinds of evidence are combined, and the
++reader should keep them apart. The first is the corpus itself: the yearly and
++monthly counts, growth rates, structural-change estimates and citation flows of
++Part~IV, read here only through the generated tables of this part
++(Tables~\ref{tab:ai_growth}--\ref{tab:ai_landmarks}) and the macros of
++\texttt{tables/numbers.tex}. The second is the automatically selected landmark
++set of the area, \SelectionByAreaAiMl{} records chosen by field-normalised
++citation impact within each primary category (Part~IV,
++Section~\ref{sec:m-score}), of which the agent read the title, venue, type,
++identifiers, citation windows and, where OpenAlex stores one, the abstract.
++The third is the verified reference list assembled in Phase~5
++(\texttt{references.bib} and \texttt{references\_part05.bib}), which carries
++the technical lineage of the field whether or not the corpus ranking surfaced
++it. Nothing in this part describes the content of a paper beyond what its
++abstract or bibliographic record states; a result known only from an abstract
++is introduced as what ``the abstract reports''. Works that exist only as
++arXiv, SSRN or medRxiv deposits are called preprints throughout, and a
++preprint's claim is never upgraded to a peer-reviewed finding by the number of
++times it has been cited. The boundary of 30~November 2022 is a reference date
++for the time-series models; the estimates below describe when the series
++changed, not why.
++
++\subsection{What the landmark set is, and what it is not}
++\label{sec:ai-landmarks}
++
++The landmark candidates of the area were screened record by record before any
++of them was used, and the outcome conditions everything that follows. Of the
++\SelectionByAreaAiMl{} records, the screening flagged 44 as unreliable for the
++purpose of a critical synthesis (Table~\ref{tab:ai_landmark_flags};
++\texttt{literature\_synthesis/agent\_E/contributions.csv}).\footnote{The
++flags are a human judgement by the specialist agent from identifiers, citation
++counts, venue, title and abstract; they were not produced by a script and
++must be audited (Part~III, Phase~8). The counts in this paragraph are those of
++the flag file, reproduced in Table~\ref{tab:ai_landmark_flags}.} Thirteen are
++\emph{identifier mismatches}: the title and abstract belong to an obscure work
++while the arXiv identifier and the 36-month citation count belong to a famous
++one. A 2026 article on translating natural language into MongoDB queries
++carries the arXiv identifier of the retrieval-augmented-generation paper
++\citep{lewis2020rag} and its citations; a Portuguese-language essay on
++copyright carries that of GPT-3 \citep{brown2020gpt3}; an essay on
++``theatrical compliance'' carries that of chain-of-thought prompting
++\citep{wei2022cot}; a preprint on an intensive-care simulation carries that of
++Llama~2 \citep{touvron2023llama2}; a ``reproducible experiment runner'' for the
++Adam optimiser carries more than forty thousand citations. Three records are
++\emph{abstract mismatches}, in which a correct title (the GPT-4 technical
++report, \citealp{openai2023gpt4}; ``GPTs are GPTs'',
++\citealp{eloundou2024gpts}) is paired with the abstract of another work.
++Eleven are \emph{implausible years}: mixup, the variational recurrent network
++and several 2013--2019 bandit papers appear as 2024--2026 works, four of them
++with April--June 2026 arXiv identifiers, so that their field-normalised scores
++are computed against the wrong cohort. Seven are \emph{normalisation
++artefacts}, dissertations and unindexed articles with two to twenty citations
++and NCS36 above five hundred, because the reference mean for a rare document
++type in a young cohort is close to zero. Ten are \emph{off scope}:
++control-theoretic consensus papers assigned to AI agents, an essay on
++educational-technology ``solutionism'' assigned to reinforcement learning.
++% [E-02, E-03]
++
++The defect is not uniformly distributed. Identifier and abstract problems
++concentrate in the large-language-model category, where eight of
++thirty-three records are affected, and misdated classics concentrate in
++machine-learning theory and deep-learning architectures. The mechanism lies
++upstream of this review, in the merging of work records by the source
++database and in the second-pass arXiv and title linkage of Phase~1
++(Section~\ref{sec:m-linkage}); it is reported here because the AI area is where
++it bites hardest, the arXiv identifiers of the most-cited papers of the decade
++being exactly the ones most likely to be attached to the wrong record. Two
++consequences are drawn. First, the clean count of citation-ranked AI landmarks
++is at most 190, and every ``most influential papers'' statement of Part~IX
++that concerns AI must be computed on records whose DOI, arXiv identifier and
++title are mutually consistent. Second, the field-normalised score should be
++recomputed with document-type-specific reference sets and with the 2026
++cohort excluded; this is a recommendation to the director, not something done
++here. % [E-04]
++
++What the set does contain is instructive in its own right
++(Table~\ref{tab:ai_contribution_types}). Judged from the abstract for 190
++records and from title, venue and type for the 44 without one, 84 of the 234
++candidates are surveys, 67 methods, 33 empirical studies, 29 theory or position
++papers, 17 engineering systems and only 4 benchmarks. % [E-05]
++Within the LLM and generative-AI categories the citation-ranked top is
++occupied by education and commentary papers written in the months after
++November 2022 \citep{kasneci2023chatgptgood, lo2023rapidreview,
++rudolph2023assessment, chan2023students, baidooanu2023chatgptedu,
++dwivedi2023sowhat, vandis2023priorities, lim2023ragnarok}, not by the papers
++that introduced the models. % [E-06]
++This is a property of ranking by field-normalised impact inside a multi-label
++category that spans computer science, education and information systems: a
++commentary in an education journal is normalised against education
++journals, and the first wave of ChatGPT commentary was cited at rates that
++no technical paper of its cohort matched within thirty-six months. The
++technical lineage---GPT-4, LLaMA, InstructGPT, chain-of-thought, DPO, QLoRA,
++BLIP-2, Mamba---is present in the set only where an arXiv record was linked,
++and sometimes, as just shown, linked to the wrong record. The synthesis
++therefore grounds its technical claims on the verified reference list and uses
++the landmark set to characterise what the corpus \emph{rewards} rather than
++what the field \emph{invented}. Table~\ref{tab:ai_landmarks} lists all
++234 candidates with their flag and contribution type.
++
++\input{tables/tab_ai_landmark_flags}
++\input{tables/tab_ai_contribution_types}
++
++\subsection{The corpus picture of the nine categories}
++\label{sec:ai-corpus}
++
++Table~\ref{tab:ai_growth} gives the primary-category volume, the compound
++annual growth rates of the baseline (2018--2022) and post (2023--2025) periods
++and the share of eligible works for each category; Table~\ref{tab:ai_counts}
++gives the yearly multi-label counts, which are several times larger because
++the AI categories overlap heavily and a work counts in every category it is
++assigned to. The ordering is the same under either definition. Large language
++models grow fastest in the post period, generative AI, multimodal AI,
++foundation models and AI safety follow, reinforcement learning and
++machine-learning theory accelerate modestly, deep-learning architectures grow
++slightly more slowly than in the baseline, and AI agents reverse a negative
++baseline growth rate. % [E-07]
++The share of eligible works carrying the LLM label as primary category rises by
++more than an order of magnitude between 2018 and 2025. % [E-08]
++Two cautions apply. The 2026 column of Table~\ref{tab:ai_counts} covers
++publications dated up to the snapshot of 14~October 2026 and is incomplete;
++and the categories are defined by rules over topics, arXiv classes, venues and
++keyword patterns (Section~\ref{sec:m-classification}), so that a work on
++reward modelling for language models is an LLM work and not a reinforcement-learning
++work, a point that matters for Section~\ref{sec:ai-rl}.
++
++\input{tables/tab_ai_growth}
++\input{tables/tab_ai_counts}
++
++The structural-change estimates are in Table~\ref{tab:ai_breaks} and, for the
++alternative event dates, Table~\ref{tab:ai_altdates}. For the LLM series the
++interrupted time series of eq.~\eqref{eq:its} fitted at
++\ItsEventsChatgpt{} gives a level change of
++\ItsSeriesCatLlmItsChatgptLevelChange{} log points
++(HAC $p = \ItsSeriesCatLlmItsChatgptLevelP$) and a slope change of
++\ItsSeriesCatLlmItsChatgptSlopeChange{} log points per month
++($p = \ItsSeriesCatLlmItsChatgptSlopeP$) over a pre-period slope of
++\ItsSeriesCatLlmItsChatgptPreSlope{}; the placebo distribution of
++Section~\ref{sec:m-placebo} places the level statistic at a placebo
++$p$-value of \ItsSeriesCatLlmPlaceboChatgptLevelTPlaceboP{} and the slope
++statistic at \ItsSeriesCatLlmPlaceboChatgptSlopeTPlaceboP{}; the
++Bai--Perron procedure selects \ItsSeriesCatLlmBaiPerronMBic{} break, dated
++in Table~\ref{tab:ai_breaks} to the middle of 2022, and the Bayesian
++on-line change-point posterior mass between December 2022 and March 2023 is
++\ItsSeriesCatLlmBocpdCpProbTwoZeroTwoTwoOneTwoToTwoZeroTwoThreeZeroThree{}.
++Excluding three months on either side of the event (the donut specification)
++changes the level estimate to
++\ItsSeriesCatLlmItsChatgptDonutLevelChange{}
++($p = \ItsSeriesCatLlmItsChatgptDonutLevelP$). % [E-09]
++The same configuration---no significant level jump at the reference month, a
++large and significant slope change, a placebo $p$-value for the level that
++does not single out December 2022, a data-driven break before or well after
++it---holds for multimodal AI (slope change
++\ItsSeriesCatMultimodalAiItsChatgptSlopeChange) and generative AI
++(\ItsSeriesCatGenerativeAiItsChatgptSlopeChange). AI agents (level
++\ItsSeriesCatAiAgentsItsChatgptLevelChange, $p =
++\ItsSeriesCatAiAgentsItsChatgptLevelP$; slope
++\ItsSeriesCatAiAgentsItsChatgptSlopeChange), reinforcement learning (level
++\ItsSeriesCatReinforcementLearningItsChatgptLevelChange, $p =
++\ItsSeriesCatReinforcementLearningItsChatgptLevelP$) and AI safety (level
++\ItsSeriesCatAiSafetyItsChatgptLevelChange, $p =
++\ItsSeriesCatAiSafetyItsChatgptLevelP$) show \emph{negative} level changes
++followed by positive slope changes, a dip and a subsequent acceleration whose
++reading is taken up category by category below. Deep-learning architectures is
++the only AI category whose slope change
++(\ItsSeriesCatDeepLearningArchitecturesItsChatgptSlopeChange, $p =
++\ItsSeriesCatDeepLearningArchitecturesItsChatgptSlopeP$) is not
++distinguishable from zero. % [E-10]
++
++\input{tables/tab_ai_breaks}
++\input{tables/tab_ai_altdates}
++
++The alternative dates of Section~\ref{sec:m-altdates} order themselves
++monotonically. Level effects estimated at the GPT-3 (\ItsEventsGptThree) and
++DALL-E~2 (\ItsEventsDalleTwo) dates are negative for every AI category,
++whereas those at the GPT-4 (\ItsEventsGptFour) and o1 (\ItsEventsOOne) dates
++are positive and significant for LLM, multimodal and generative AI: for the
++LLM series, \ItsSeriesCatLlmItsGptThreeLevelChange{} at the GPT-3 date
++($p = \ItsSeriesCatLlmItsGptThreeLevelP$), \ItsSeriesCatLlmItsGptFourLevelChange{}
++at the GPT-4 date ($p = \ItsSeriesCatLlmItsGptFourLevelP$) and
++\ItsSeriesCatLlmItsOOneLevelChange{} at the o1 date
++($p = \ItsSeriesCatLlmItsOOneLevelP$). % [E-11]
++Each of these is a separate fit with the event moved, and a positive level
++effect at a later date in a convex series is what a steepening trend produces
++whether or not anything happened at that date. The reading that the evidence
++supports is therefore restricted: the acceleration of the AI literature is a
++change of trend that the data-driven procedures place in the middle of 2022
++and that steepened through 2023--2024; the monthly series cannot attribute it
++to one product release, and this part does not. % [E-12]
++
++\subsection{Knowledge flows and detected emergence}
++\label{sec:ai-flows}
++
++Table~\ref{tab:ai_flows} reports, for each AI category and for 2019, 2022 and
++2025, the destination of its in-corpus references, i.e.\ the row shares
++$s_{c \to c'}(t)$ of eq.~\eqref{eq:flow}. The large-language-model category is
++the one whose profile changed most. In 2019 and 2022 roughly seven in ten of
++its in-corpus references went to natural language processing; in 2025 about a
++third do, while more than a quarter go to the LLM category itself and
++measurable shares go to human--computer interaction and software engineering.
++% [E-13]
++Generative AI moved from a vision-centred profile to one split between vision,
++LLM and HCI; AI safety, which cited LLM work at a negligible rate in 2019,
++directs a tenth of its references there in 2025 and slightly more to HCI.
++% [E-14]
++Reinforcement learning moves the other way, citing robotics more and LLM work
++almost not at all, and machine-learning theory and deep-learning architectures
++are essentially unchanged. % [E-15]
++At the level of categories, then, the LLM literature has become a citation hub
++for generative AI, safety and agents while remaining peripheral to
++reinforcement learning, theory and architectures. This is the corpus-level
++answer to RQ5 that this part can give; it is consistent with the convergence
++thesis of \citet{bommasani2021foundation}, but a change in which category a
++field cites is not evidence that its methods converged, and
++Section~\ref{sec:ai-synthesis} returns to the distinction. % [E-16]
++
++\input{tables/tab_ai_flows}
++
++The preregistered known-emergence set of Section~\ref{sec:m-emerging} contains
++eleven AI concepts, and their lead times are the first empirical test of
++RQ10 in this area. The detector flags \emph{large language models} in
++\KnownLargeLanguageModelsDetectionMonth{},
++\KnownLargeLanguageModelsLeadMonths{} months before the peak-growth month
++\KnownLargeLanguageModelsPeakGrowthMonth{}, on
++\KnownLargeLanguageModelsTotalWorks{} matching works; \emph{reasoning models}
++\KnownReasoningModelsLeadMonths{} months ahead; retrieval-augmented generation
++\KnownRetrievalAugmentedGenerationLeadMonths{} months; vision-language models
++\KnownVisionLanguageModelsLeadMonths{}; diffusion models
++\KnownDiffusionModelsLeadMonths{}; LLM agents \KnownLlmAgentsLeadMonths{};
++state-space sequence models \KnownStateSpaceSequenceModelsLeadMonths{};
++mixture of experts \KnownMixtureOfExpertsLeadMonths{}; speculative decoding
++\KnownSpeculativeDecodingLeadMonths{}; AI code generation
++\KnownAiCodeGenerationLeadMonths{}; and mechanistic interpretability
++\KnownMechanisticInterpretabilityLeadMonths{}, a negative lead in which the
++peak-growth month precedes detection on only
++\KnownMechanisticInterpretabilityTotalWorks{} matching works. The median lead
++over the whole set is \KnownSetMedianLeadMonths{} months. % [E-17]
++The long lead for language models is not foresight: the regular expression
++matches the pre-2020 literature on ``language models'' and the detector fires
++on its slow growth years before the 2023 surge. The short leads for
++state-space models, mixture of experts and speculative decoding are the
++honest result, and they say that topics driven by a single widely adopted
++preprint are detected only months before their growth peaks.
++
++\section{Large language models and foundation models}
++\label{sec:ai-fm}
++
++\subsection{The established lineage}
++
++The architecture that dominates the window is the decoder-only Transformer
++\citep{vaswani2017attention} trained autoregressively at scale
++\citep{radford2019gpt2, brown2020gpt3, chowdhery2022palm}, with the encoder
++and encoder--decoder variants of the baseline period \citep{devlin2019bert,
++raffel2020t5} surviving mainly as embedding models. Two empirical regularities
++from before the window organise the technical literature inside it. The first
++is the scaling law, in its original form a power law of loss in parameters $N$
++and tokens $D$ \citep{kaplan2020scaling} and in the compute-optimal form of
++\citet{hoffmann2022chinchilla}, which, for a fixed training budget $C \approx
++6ND$, allocates roughly equal factors of growth to $N$ and $D$ and therefore
++implies far more tokens per parameter than the earlier prescription. The
++second is the sparse mixture-of-experts layer \citep{shazeer2017moe,
++fedus2022switch}, which decouples parameter count from per-token compute. The
++open-weight lineage LLaMA, Llama~2 and Llama~3 \citep{touvron2023llama,
++touvron2023llama2, dubey2024llama3}---all three preprints, the first of which
++is in the landmark set---follows the Chinchilla allocation; the LLaMA abstract
++reports that a 13-billion-parameter model trained only on publicly available
++data outperforms GPT-3 on most benchmarks, a claim that the abstract makes and
++that this review has not verified. % [E-18]
++The closed frontier releases are documented by technical reports without
++training details \citep{openai2023gpt4, gemini2023, openai2024o1}; they are
++cited here as the dated artefacts they are, and the GPT-4 record in the
++landmark set, as noted above, carries someone else's abstract. The LLM surveys
++of the window \citep{zhao2023llmsurvey, naveed2023llmoverview,
++minaee2024llmsurvey, min2023plmsurvey} map a sub-literature that by 2025 had
++its own citation core (Table~\ref{tab:ai_flows}); the journal version of the
++first of them is in the landmark set and its abstract organises the field
++into pre-training, adaptation, utilisation and evaluation.
++
++The most visible post-2022 development in the landmark set is not a new
++general model but the diffusion of the foundation-model recipe into domains
++with their own data. Self-supervised pathology encoders trained on hundreds of
++thousands of whole-slide images \citep{chen2024uni, lu2024conch,
++wang2024chief, huang2023plip, ding2025titan}, a radiology model trained on
++two- and three-dimensional scans \citep{wu2025radfm}, a dermatology model on
++two million clinical images \citep{yan2025panderm}, a brain-MRI model
++\citep{tak2026brainiac}, an Earth-system model trained on more than a million
++hours of geophysical data \citep{bodnar2025aurora}, a tabular model that the
++abstract describes as a prior-data fitted network outperforming
++gradient-boosted trees on small datasets \citep{hollmann2025tabpfn},
++time-series representation models \citep{jin2023timellm, liu2024timesurl} and
++a language model fine-tuned on ten million human choices to predict behaviour
++across psychological experiments \citep{binz2025centaur} are, with the
++exception of the time-series preprint, peer-reviewed journal or conference
++papers, and each abstract reports outperforming task-specific baselines on a
++battery of benchmarks. % [E-19]
++What the abstracts do not report, and what the reviews in the same set list as
++unresolved \citep{paschali2025fmradiology, zhang2026datacentric}, is external,
++multi-site validation under distribution shift; the claim that domain
++foundation models will displace task-specific pipelines in regulated settings
++is speculative and is carried to Part~X as such. % [E-24]
++
++Below the frontier, the practical growth of the open-weight ecosystem
++concentrated on adaptation. Low-rank adaptation \citep{hu2021lora}, its
++four-bit variant QLoRA, whose abstract reports fine-tuning a 65-billion-parameter
++model on a single 48\,GB GPU \citep{dettmers2023qlora}, the zero-initialised
++adaption prompts of LLaMA-Adapter \citep{zhang2023llamaadapter} and the
++parameter-efficient fine-tuning surveys \citep{han2024peftsurvey} define a
++regime in which a research group without frontier compute can specialise a
++model in hours. The QLoRA abstract's statement that its best model reaches
++``99.3\,\% of the performance level of ChatGPT'' on the Vicuna benchmark is a
++judge-based measurement, a point to which Section~\ref{sec:ai-eval} returns.
++% [E-20]
++
++\subsection{Contested claims: emergence, sparks and pure-RL reasoning}
++
++Three claims about what scaled language models are have been argued largely in
++preprints, and the corpus can record the argument but not settle it. The first
++is emergence. \citet{wei2022emergent} report abilities that appear
++discontinuously as scale increases; \citet{schaeffer2023mirage} show that for
++the tasks examined the discontinuities are produced by discontinuous
++metrics---exact-match accuracy on multi-token answers---and largely vanish
++under continuous or token-level ones. Both are preprints, the disagreement is
++about measurement rather than data, and the manuscript presents it as such.
++% [E-21]
++The second is ``Sparks of Artificial General Intelligence''
++\citep{bubeck2023sparks}, which reports qualitative experiments on an early,
++unreleased version of GPT-4 and, in the abstract's words, contends that the
++model exhibits more general intelligence than previous systems. The record is
++among the most cited of the landmark set, it is a preprint, its experiments
++are not reproducible because the model version is unavailable, and the
++critique that scale does not confer understanding \citep{bender2021parrots}
++is in the same set with a comparable citation count. % [E-22]
++The third is the reasoning turn: the o1 system card \citep{openai2024o1}
++moved computation to inference time, and the DeepSeek-R1 paper, whose
++peer-reviewed version is in the landmark set \citep{deepseek2025r1nature}
++alongside the preprint \citep{deepseek2025r1}, reports that reasoning ``can be
++incentivized through pure reinforcement learning, obviating the need for
++human-labelled reasoning trajectories''. The claim is peer reviewed; its
++generality beyond domains with verifiable rewards---mathematics, code---is not
++tested by anything in the corpus, and it is the first entry of the open
++problems in Section~\ref{sec:ai-synthesis}. % [E-23]
++
++\section{Alignment and post-training}
++\label{sec:ai-align}
++
++The recipe that turned a pretrained language model into an assistant was in
++place before the window: a reward model fitted to human preference
++comparisons \citep{christiano2017rlhf} and a policy optimised against it with
++a proximal objective \citep{schulman2017ppo}, applied to instruction following
++in InstructGPT \citep{ouyang2022instructgpt} and extended with helpfulness and
++harmlessness data \citep{bai2022hh} and with model-written critiques against a
++written constitution \citep{bai2022constitutional}. Inside the window the
++method's two moving parts were simplified and questioned. Direct preference
++optimisation \citep{rafailov2023dpo} rewrites the RLHF objective so that the
++policy is fitted directly to preference pairs through a closed-form
++log-ratio loss, removing the separate reward model and the on-policy sampling;
++the NeurIPS record is in the landmark set, without an abstract in the file.
++The open problems of the reward-model route---reward hacking, preference
++mis-specification, evaluator disagreement, the gap between stated and revealed
++preferences---are catalogued by \citet{casper2023rlhfproblems}, a preprint
++whose taxonomy is nevertheless the standard reference. % [E-25]
++The corpus adds a sociological observation to this methodological one.
++Reinforcement learning as a category directs less than one per cent of its
++in-corpus references to LLM work in 2025 (Table~\ref{tab:ai_flows}), while the
++LLM category's own self-citation share grew to more than a quarter. The
++``return of reinforcement learning'' through post-training is therefore a
++phenomenon of the LLM literature, which absorbed PPO, DPO and the group-relative
++policy optimisation of DeepSeek-R1 as its own methods; the classification
++separates the two communities even where the mathematics is shared, and
++Section~\ref{sec:ai-rl} draws the consequence for any claim about the fate of
++reinforcement learning.
++
++That aligned behaviour is brittle under optimisation pressure is established
++by three lines of work. Universal adversarial suffixes found by gradient-guided
++search transfer across models \citep{zou2023universal}; the ``jailbroken''
++analysis attributes failures to competing objectives and mismatched
++generalisation between pretraining and safety training
++\citep{wei2023jailbroken}; and PAIR, a peer-reviewed method in the landmark
++set, is described in its abstract as an attacker language model that generates
++semantic jailbreaks against a target with black-box access, in about twenty
++queries \citep{chao2025pair}. % [E-26]
++The first two are preprints and the third was published in 2025; together they
++show that every public alignment method of the window can be defeated by a
++modest adaptive budget. Whether this brittleness is intrinsic to
++preference-based alignment or an artefact of current scale and data is the
++question that none of the landmark abstracts answers, and it is carried to
++Part~X.
++
++\section{Reasoning, retrieval, tool use and code}
++\label{sec:ai-reason}
++
++Chain-of-thought prompting \citep{wei2022cot, kojima2022zeroshotcot},
++tree-structured search over thoughts \citep{yao2023tot} and verbal
++self-reflection across episodes \citep{shinn2023reflexion} are the prompting
++lineage of the reasoning literature; their evidence is benchmark gains, mostly
++on arithmetic and symbolic tasks, and several of the records are preprints.
++The reasoning-model turn of 2024--2025 \citep{openai2024o1,
++deepseek2025r1nature} internalised the procedure by training the model to
++produce long intermediate traces and by spending compute at inference rather
++than at training. The detector of Section~\ref{sec:m-emerging} flagged
++\emph{reasoning models} in \KnownReasoningModelsDetectionMonth{},
++\KnownReasoningModelsLeadMonths{} months before the peak-growth month of
++\KnownReasoningModelsPeakGrowthMonth{}; the long lead is an artefact of a
++regular expression that includes chain-of-thought and therefore matched the
++2023 prompting literature, and the honest reading is that the concept's
++vocabulary existed well before its models. % [E-17]
++
++Retrieval augmentation is the most widely adopted of the window's patterns.
++The dense passage retriever \citep{karpukhin2020dpr} and the
++retrieval-augmented generation architecture \citep{lewis2020rag} precede the
++window; their adoption inside it is surveyed by \citet{gao2023ragsurvey}, and
++the known-set detector counts \KnownRetrievalAugmentedGenerationTotalWorks{}
++matching works, detected in \KnownRetrievalAugmentedGenerationDetectionMonth{}
++with peak growth in \KnownRetrievalAugmentedGenerationPeakGrowthMonth{}.
++Tool use---ReAct's interleaving of reasoning traces and actions
++\citep{yao2023react}, Toolformer's self-supervised API calls
++\citep{schick2023toolformer}, ToolLLM's instruction tuning over thousands of
++real APIs \citep{qin2023toolllm}, surveyed by \citet{mialon2023augmented}---is
++the bridge from language models to the agents of Section~\ref{sec:ai-agents};
++all four are preprints. Hallucination, the generation of fluent content
++unsupported by the input or by fact, is the failure mode that these
++mechanisms are meant to contain, and it remains a measurement problem before
++it is a modelling problem: the surveys \citep{ji2023hallucination,
++huang2025hallucination} agree on taxonomies and disagree on metrics. The
++clearest statement of why is in the landmark set. The Med-PaLM paper
++\citep{singhal2023clinical} built MultiMedQA from six medical
++question-answering datasets and a new set of consumer questions, and its
++abstract reports that a human evaluation framework along axes of factuality,
++comprehension, reasoning and harm was needed because automated evaluation
++against limited benchmarks did not meet the bar for clinical use. % [E-27]
++
++Code generation is the application in which the window's models were first
++measured against professional work. The Codex evaluation
++\citep{chen2021codex}, AlphaCode's competition-level results
++\citep{li2022alphacode}, the open code models \citep{nijkamp2023codegen,
++roziere2023codellama, li2023starcoder} and the SWE-bench task of resolving
++real repository issues \citep{jimenez2024swebench} define the benchmark
++sequence; the controlled productivity experiment of \citet{peng2023copilot}
++and the field data of \citet{ziegler2022copilot} are the usage evidence, with
++the usability and security caveats of \citet{vaithilingam2022expectation},
++\citet{barke2023groundedcopilot} and \citet{pearce2022asleep}. The detector's
++lead for \emph{AI code generation} is \KnownAiCodeGenerationLeadMonths{}
++months. The software-engineering reading of this literature belongs to
++Part~VII; here it is noted only that the LLM category's references to software
++engineering in 2025 (Table~\ref{tab:ai_flows}) are the trace of this
++application in the citation graph.
++
++\section{Multimodal and generative AI}
++\label{sec:ai-multimodal}
++
++\subsection{Vision--language models}
++
++Two baseline-period results make the multimodal literature of the window
++possible: contrastive image--text pre-training, which aligns an image encoder
++and a text encoder on web-scale pairs \citep{radford2021clip}, and the vision
++Transformer, which made the image encoder the same object as the text model
++\citep{dosovitskiy2021vit}. The single-stream alternative, in which words and
++image regions are tokens of one Transformer, is represented in the landmark
++set by VL-BERT \citep{su2019vlbert}; the taxonomy of representation,
++translation, alignment, fusion and co-learning that the field still uses is
++that of \citet{baltrusaitis2019multimodal}, also in the set. Inside the window
++the dominant design is a frozen or lightly adapted language model fed by a
++visual encoder through a small bridge: a perceiver resampler in Flamingo
++\citep{alayrac2022flamingo}; a querying Transformer trained in two stages
++between frozen encoders and frozen LLMs in BLIP-2, whose abstract motivates
++the design by the prohibitive cost of end-to-end pre-training
++\citep{li2023blip2}; instruction tuning of the same bridge in InstructBLIP
++\citep{dai2023instructblip}; a linear projection in LLaVA
++\citep{liu2023llava}; shared functional modules with the language decoder as
++``universal interface'' in mPLUG-Owl2 \citep{ye2024mplugowl2}. The promptable
++segmentation model \citep{kirillov2023sam} is the dense-prediction analogue,
++and the open image--text corpus LAION-5B \citep{schuhmann2022laion5b} is what
++made CLIP-scale training reproducible outside industry. % [E-28]
++Of these records, BLIP-2, InstructBLIP, LLaVA and LAION-5B are preprint or
++proceedings deposits; mPLUG-Owl2 is a CVPR paper. The surveys
++\citep{yin2024mllmsurvey, zhang2024mmllms} organise the design space; the
++corpus-level trace is the vision-language detector, which fires
++\KnownVisionLanguageModelsLeadMonths{} months before the concept's peak growth.
++
++Whether these models \emph{fuse} modalities or merely \emph{condition} on the
++text channel is the open question that the landmark abstracts raise most
++sharply. An evaluation of eight proprietary and open multimodal foundation
++models on 1,090 medical cases reports, in its abstract, that image predictions
++are ``largely driven by text'', that accuracy rises monotonically with the
++amount of informative text, and that even mild textual suggestions of an
++incorrect diagnosis degrade image-based classification
++\citep{buckley2026textexploit}. % [E-30]
++The scoping review of 432 medical multimodal papers \citep{schouten2025multimodalmedicine}
++and the survey of fusion under noisy, missing and imbalanced modalities
++\citep{zhang2026lowqualityfusion} describe the same robustness gap from the
++methods side. The medical records in the set---generalist biomedical and
++radiology models \citep{zhang2024biomedgpt, wu2025radfm}, reinforcement-learning
++fine-tuning for medical reasoning in vision-language models and a zero-shot
++evaluation of GPT-5 on medical visual question answering, the latter a
++preprint \citep{lai2026medr1, wang2025gpt5medical}---report benchmark gains
++but not modality ablations; the question is carried to Part~X.
++
++\subsection{Diffusion models and their applications}
++
++The generative lineage of the window is diffusion. Denoising diffusion
++probabilistic models \citep{ho2020ddpm} and their latent-space formulation
++\citep{rombach2022ldm} precede the boundary; Imagen \citep{saharia2022imagen}
++and DALL-E~2 \citep{ramesh2022dalle2} are the 2022 text-to-image systems,
++and the Imagen record is in the landmark set without an abstract. Three
++technical contributions in the set define how the models are controlled.
++Classifier-free guidance \citep{ho2022cfg}, a preprint, trains the conditional
++and unconditional score jointly and interpolates between them at sampling
++time, removing the separate classifier that earlier guidance required;
++textual inversion \citep{gal2022textualinversion}, also a preprint, learns a
++new token embedding from three to five images of a concept so that a frozen
++model can render it; T2I-Adapter \citep{mou2024t2iadapter}, an AAAI paper,
++trains small adapters that align external structural or colour signals with
++the internal knowledge of a frozen text-to-image model. The Transformer
++backbone for diffusion \citep{peebles2023dit} and its rectified-flow
++successor \citep{esser2024sd3}, a preprint, close the architectural loop by
++replacing the U-Net. The detector flags \emph{diffusion models}
++\KnownDiffusionModelsLeadMonths{} months before peak growth. Outside images,
++the one peer-reviewed scientific application in the landmark set is
++MatterGen \citep{zeni2025mattergen}, whose abstract reports a diffusion model
++that generates stable, diverse inorganic materials across the periodic table
++and can be fine-tuned towards property constraints. % [E-29]
++Speech followed the same self-supervised path a few years earlier
++\citep{baevski2020wav2vec2, hsu2021hubert} and reached general-purpose
++recognition with weakly supervised training at scale \citep{radford2023whisper};
++the review of \citet{mehrish2023speechreview} covers the category, which the
++taxonomy places outside the nine categories of this part.
++
++\subsection{What the generative-AI category rewards}
++
++Generative AI is the category whose citation profile changed most inside the
++window, from a vision-centred one to a three-way split between vision, LLM
++and HCI (Table~\ref{tab:ai_flows}). Its citation-ranked landmarks are,
++however, not the diffusion papers just described but education, policy and
++adoption studies: a policy framework from surveys of students and staff
++\citep{chan2023policyframework}, scoping and systematic reviews of assessment
++and academic integrity \citep{xia2024assessmentreview, bittle2025integrity},
++analyses of the guidelines of 116 and of 50 United States universities
++\citep{mcdonald2025hepolicies}, a paradox-theory essay \citep{lim2023ragnarok},
++an OECD outlook, and two economics papers: a preregistered field experiment,
++circulated as an SSRN preprint, whose title states that generative AI can harm
++learning \citep{bastani2024harmlearning}, and nationally representative
++surveys whose abstract reports that 45 per cent of United States adults aged
++18--64 used generative AI by late 2024 and that work adoption was faster than
++that of the personal computer \citep{bick2026rapidadoption}. % [E-31]
++The pattern repeats the one observed for LLMs in Section~\ref{sec:ai-landmarks}:
++technical generative-AI papers are counted in the category but, normalised
++against their own cohort, rarely reach its citation top within thirty-six
++months, whereas the first social-science and education responses to the
++technology do. A reader who takes the landmark list as the field's
++intellectual map would conclude that generative AI is a topic in education
++research; the correct conclusion is that field normalisation inside a
++multi-label category measures discourse, and that the measurement is
++informative about diffusion rather than about invention.

modified manuscript/sections/part08_networks.tex +37 −33

				`@@ -224,10 +224,10 @@ universal outside the AI/ML area. Within the AI/ML area the rise is carried by t`
224	reinforcement learning, deep-learning architectures and agents, while the categories that define the post	224	reinforcement learning, deep-learning architectures and agents, while the categories that define the post
225	period behave differently: the share is nearly flat for large language models, falls for foundation models and	225	period behave differently: the share is nearly flat for large language models, falls for foundation models and
226	generative AI, and is highest of all forty categories, in both periods, for multimodal AI. The United States	226	generative AI, and is highest of all forty categories, in both periods, for multimodal AI. The United States
227	−share falls in both groups and in most categories, with large language models the one AI/ML category in	227	+share falls in both groups and in most categories; agents and large language models are the AI/ML
228	−which it rises. The yearly series of Table~\ref{tab:net_ai_vs_rest_yearly} show the pooled shares of the two	228	+categories in which it rises. The yearly series of Table~\ref{tab:net_ai_vs_rest_yearly} show the pooled
229	−groups moving together: both rise through 2023 and fall in 2024 and 2025, and the two series are closer to	229	+shares of the two groups moving together: both rise through 2023 and fall in 2024 and 2025, and the gap
230	−each other in every post-period year than in any baseline year. The fall after 2023 coincides with the lower	230	+between them, roughly constant through 2023, closes in 2024 and 2025. The fall after 2023 coincides with the lower
231	affiliation coverage of recent cohorts (Section~\ref{sec:net-teams}) and cannot be read as a reversal; what	231	affiliation coverage of recent cohorts (Section~\ref{sec:net-teams}) and cannot be read as a reversal; what
232	it does establish is that the AI/ML area is not where the China share grows fastest. The verdict on this	232	it does establish is that the AI/ML area is not where the China share grows fastest. The verdict on this
233	component of H6 is therefore a supported direction with an unsupported specificity: the share rises in AI	233	component of H6 is therefore a supported direction with an unsupported specificity: the share rises in AI
				`@@ -294,15 +294,15 @@ falling in the aggregate, is exactly the kind of composition question that the a`
294	Tables~\ref{tab:net_category_actors}, \ref{tab:net_ai_vs_rest} and~\ref{tab:net_ai_vs_rest_yearly} settle it	294	Tables~\ref{tab:net_category_actors}, \ref{tab:net_ai_vs_rest} and~\ref{tab:net_ai_vs_rest_yearly} settle it
295	in the negative. The company share of the AI/ML area is higher than that of the other areas in every year,	295	in the negative. The company share of the AI/ML area is higher than that of the other areas in every year,
296	which is the familiar result that industry is more present in machine learning than in the rest of computer	296	which is the familiar result that industry is more present in machine learning than in the rest of computer
297	−science; but it peaks in 2021 and falls in every subsequent year, so that the baseline-to-post decline of the	297	+science; but it peaks in 2021 and falls in every subsequent complete year, so that the baseline-to-post decline of the
298	AI/ML area is larger than that of the other areas, not smaller. The note of	298	AI/ML area is larger than that of the other areas, not smaller. The note of
299	Table~\ref{tab:net_category_actors} counts the categories whose company share rises: one of the nine AI/ML	299	Table~\ref{tab:net_category_actors} counts the categories whose company share rises: one of the nine AI/ML
300	categories (agents), against a sizeable minority of the others, among them cloud computing, information	300	categories (agents), against a sizeable minority of the others, among them cloud computing, information
301	−retrieval, distributed computing, database systems, operating systems and quantum computing. The four	301	+retrieval, distributed computing, database systems, operating systems and quantum computing. The AI/ML
302	−categories with the highest baseline company shares, multimodal AI, foundation models, reinforcement	302	+categories with the highest baseline company shares, multimodal AI, reinforcement learning, foundation
303	−learning and large language models, all lose share in the post period, and generative AI loses more than	303	+models and large language models, all lose share in the post period, and generative AI loses more than
304	half of its share. The denominator caveat of the previous paragraph applies with more force here than for the	304	half of its share. The denominator caveat of the previous paragraph applies with more force here than for the
305	−China share, because the AI/ML area has the highest and fastest-growing share of arXiv-linked works	305	+China share, because the AI/ML area has the highest share of arXiv-linked works
306	(Table~\ref{tab:net_ai_vs_rest}) and preprints are the records least often enriched with affiliations; part of	306	(Table~\ref{tab:net_ai_vs_rest}) and preprints are the records least often enriched with affiliations; part of
307	the measured decline is therefore coverage. Nothing in the category tables, however, suggests the opposite	307	the measured decline is therefore coverage. Nothing in the category tables, however, suggests the opposite
308	sign for the AI categories, and the frontier-share results of \citet{ahmed2023industry} remain the only	308	sign for the AI categories, and the frontier-share results of \citet{ahmed2023industry} remain the only
				`@@ -462,14 +462,15 @@ baseline value before 2021, so the aggregate evidence is mixed and dominated by`
462	version of the test, which is the one the hypothesis states, is in Table~\ref{tab:net_category_gini}, which	462	version of the test, which is the one the hypothesis states, is in Table~\ref{tab:net_category_gini}, which
463	gives the Gini of $c^{(36)}$ by primary category for every publication year with a complete window, and in	463	gives the Gini of $c^{(36)}$ by primary category for every publication year with a complete window, and in
464	Table~\ref{tab:net_gini_ai_vs_rest}, which pools the AI/ML area against the other areas as work-weighted means	464	Table~\ref{tab:net_gini_ai_vs_rest}, which pools the AI/ML area against the other areas as work-weighted means
465	−of the category statistics. Two patterns are visible. Across the baseline the within-category Gini falls in	465	+of the category statistics. Two patterns are visible. Between the 2018 and 2022 cohorts the within-category Gini falls in
466	−almost every category, as the aggregate does, and for the same reason, the falling share of uncited works.	466	+almost every category, as the aggregate does, and for the same reason, the falling share of uncited works;
		467	+the exceptions are algorithms and large language models.
467	For the 2023 cohort the pooled AI/ML Gini is above its baseline mean while the pooled Gini of the other	468	For the 2023 cohort the pooled AI/ML Gini is above its baseline mean while the pooled Gini of the other
468	areas is below its own; the note of Table~\ref{tab:net_category_gini} counts the categories whose 2023 value	469	areas is below its own; the note of Table~\ref{tab:net_category_gini} counts the categories whose 2023 value
469	exceeds their baseline mean, and the proportion is far higher among the AI/ML categories than among the	470	exceeds their baseline mean, and the proportion is far higher among the AI/ML categories than among the
470	−others. Large language models have the highest Gini of any category in every year, and theirs rises in every	471	+others. Large language models have the highest Gini of any category in every year from 2019 onward (the
471	−year of the window, baseline included, which is the one category-level series that moves against the	472	+implausible 2023 value of formal verification apart), and theirs ends the baseline above where it began,
472	−baseline decline. This is evidence in the direction of H5, and it is weak evidence: it rests on a single	473	+which makes it the clearest category-level exception to the baseline decline. This is evidence in the direction of H5, and it is weak evidence: it rests on a single
473	post-period cohort whose window is affected by truncation, the uncited share of that cohort rises by about	474	post-period cohort whose window is affected by truncation, the uncited share of that cohort rises by about
474	the same amount in both groups, so the difference between them is a tail effect rather than a zeros effect,	475	the same amount in both groups, so the difference between them is a tail effect rather than a zeros effect,
475	and the small categories, together with the implausible 2023 value for formal verification, show the	476	and the small categories, together with the implausible 2023 value for formal verification, show the
				`@@ -656,7 +657,7 @@ become more self-referential and more recent-leaning over the 2010s.`
656		657
657	Figure~\ref{fig:cited_age} and Table~\ref{tab:cited_age} give the distribution of the age of cited works, the	658	Figure~\ref{fig:cited_age} and Table~\ref{tab:cited_age} give the distribution of the age of cited works, the
658	citing year minus the cited work's publication year, for the resolved references of eligible works, read	659	citing year minus the cited work's publication year, for the resolved references of eligible works, read
659	−from the corpus v3 file \texttt{results/graph/cited\_age\_yearly.csv}, which is built on the same edge sets as	660	+from the corpus v3 file \texttt{results/\allowbreak graph/\allowbreak cited\_age\_yearly.csv}, which is built on the same edge sets as
660	Table~\ref{tab:reference_coverage}. The mode of citation age is two years in every citing year (visible in	661	Table~\ref{tab:reference_coverage}. The mode of citation age is two years in every citing year (visible in
661	the figure, where every curve peaks at the same abscissa); the mean age falls in every citing year from 2018	662	the figure, where every curve peaks at the same abscissa); the mean age falls in every citing year from 2018
662	to 2026; the median falls over the baseline years and is constant from 2021 onward; the share of	663	to 2026; the median falls over the baseline years and is constant from 2021 onward; the share of
				`@@ -674,7 +675,7 @@ become first-appearance dates, and it is excluded from the age statistics.`
674	\includegraphics[width=0.95\textwidth]{fig_cited_age.pdf}	675	\includegraphics[width=0.95\textwidth]{fig_cited_age.pdf}
675	\caption{Distribution of the age of cited works (citing year minus cited year, 0 to 30 years) for five	676	\caption{Distribution of the age of cited works (citing year minus cited year, 0 to 30 years) for five
676	citing years. Generated by \texttt{figures/make\_figures.py} from	677	citing years. Generated by \texttt{figures/make\_figures.py} from
677	−\texttt{results/graph/cited\_age\_yearly.csv} (corpus v3); summary statistics in Table~\ref{tab:cited_age}.}	678	+\texttt{results/\allowbreak graph/\allowbreak cited\_age\_yearly.csv} (corpus v3); summary statistics in Table~\ref{tab:cited_age}.}
678	\label{fig:cited_age}	679	\label{fig:cited_age}
679	\end{figure}	680	\end{figure}
680		681
				`@@ -724,16 +725,16 @@ to the cited area (columns), per cent. Source: \texttt{results/graph/area\_flows`
724	\label{fig:area_flow_matrix}	725	\label{fig:area_flow_matrix}
725	\end{figure}	726	\end{figure}
726		727
727	−The area matrices are diagonal-dominant in both periods: every area sends the majority of its internal	728	+The area matrices are diagonal-dominant in both periods: every area but one sends the majority of its
728	−references to itself, from about two thirds for the security and software areas to a much higher share for	729	+internal references to itself, from about two thirds for the security, software, theory and
729	−the perception area, which is the largest area and the one whose categories (vision, language, speech,	730	+computational-science areas to a much higher share for the perception area, which is the largest area and the one whose categories (vision, language, speech,
730	robotics, interaction, graphics) cite each other most. The largest off-diagonal flows are structural and do	731	robotics, interaction, graphics) cite each other most. The largest off-diagonal flows are structural and do
731	not change between periods: the AI/ML area sends about a third of its references to the perception area,	732	not change between periods: the AI/ML area sends about a third of its references to the perception area,
732	which is where the application work on vision and language that the machine-learning categories build on	733	which is where the application work on vision and language that the machine-learning categories build on
733	is classified; the security area sends about a sixth of its references to the data and distributed area, the	734	is classified; the security area sends about a sixth of its references to the data and distributed area, the
734	networks and cloud work that security builds on; and computational science splits its outside references	735	networks and cloud work that security builds on; and computational science splits its outside references
735	between AI/ML and perception. What changes between periods is the AI/ML column. The self-share of every	736	between AI/ML and perception. What changes between periods is the AI/ML column. The self-share of every
736	−area except perception falls, and the share of references each of these areas sends to the AI/ML area rises,	737	+area except perception and hardware falls, and the share of references each area sends to the AI/ML area rises,
737	the increments being largest for the software area, computational science, security and the data and	738	the increments being largest for the software area, computational science, security and the data and
738	distributed area (Table~\ref{tab:net_area_flows}; the post-minus-baseline differences are the subject of	739	distributed area (Table~\ref{tab:net_area_flows}; the post-minus-baseline differences are the subject of
739	Part~IX). The AI/ML area's own self-share falls as well, in favour of the perception area: the	740	Part~IX). The AI/ML area's own self-share falls as well, in favour of the perception area: the
				`@@ -748,10 +749,11 @@ stated, since the hypothesis names systems, security and human--computer interac`
748	that should cite the AI categories more. All three do. The share of references going to the AI/ML area	749	that should cite the AI categories more. All three do. The share of references going to the AI/ML area
749	rises in human--computer interaction, software engineering, cybersecurity, database systems, distributed	750	rises in human--computer interaction, software engineering, cybersecurity, database systems, distributed
750	computing, computer networks and computer architecture, in each case from the beginning of the window,	751	computing, computer networks and computer architecture, in each case from the beginning of the window,
751	−and in each case the post-period values are above every baseline value. The timing differs across these	752	+and in each case the values of 2024 and 2025 are at or above every baseline value. The timing differs across these
752	−series in a way the hypothesis does not predict. For human--computer interaction and software engineering	753	+series in a way the hypothesis does not predict. For human--computer interaction the series is nearly flat
753	−the series is nearly flat through 2023 and rises steeply in 2024, 2025 and the incomplete 2026, so the	754	+through 2023 and rises steeply in 2024, 2025 and the incomplete 2026; for software engineering the rise
754	−change is concentrated in the second and third post-period years rather than at the boundary, consistent	755	+begins in 2021 and steepens from 2023; in both the change is concentrated in the second and third
		756	+post-period years rather than at the boundary, consistent
755	with the lag between the appearance of a model and the appearance of empirical work that cites it; for	757	with the lag between the appearance of a model and the appearance of empirical work that cites it; for
756	cybersecurity, database systems and distributed computing the post-period increments continue the baseline	758	cybersecurity, database systems and distributed computing the post-period increments continue the baseline
757	pace; for computer networks and computer architecture the share is the lowest among the systems categories	759	pace; for computer networks and computer architecture the share is the lowest among the systems categories
				`@@ -762,7 +764,7 @@ category and was cited as NLP work rather than as machine learning, and rises st`
762	large-language-model category (which the ontology places in the AI/ML area) absorbs the work the category	764	large-language-model category (which the ontology places in the AI/ML area) absorbs the work the category
763	now builds on. Computer vision shows no such rise, which again reflects the classifier's boundary: vision	765	now builds on. Computer vision shows no such rise, which again reflects the classifier's boundary: vision
764	work citing vision models is a within-category flow. The theory categories rise modestly, and	766	work citing vision models is a within-category flow. The theory categories rise modestly, and
765	−computational biology and robotics return in the post period to the shares they had in 2018. These are	767	+computational biology and robotics return in the post period to the levels of the earliest, sparse years. These are
766	flows of references between classified works; they say that the systems, security and interaction	768	flows of references between classified works; they say that the systems, security and interaction
767	categories build on AI/ML-classified work more than they did, which is what H4 asserts, and not that the	769	categories build on AI/ML-classified work more than they did, which is what H4 asserts, and not that the
768	work itself has become AI research, which is a question about classification rather than citation.	770	work itself has become AI research, which is a question about classification rather than citation.
				`@@ -786,8 +788,9 @@ language, are placed in a different area, and the statistic would be lower under`
786	machine learning with its application fields. This dependence on the partition is the reason	788	machine learning with its application fields. This dependence on the partition is the reason
787	Section~\ref{sec:m-networks} describes $X_{\alpha,t}$ as a boundary-crossing rate and not as a measure of	789	Section~\ref{sec:m-networks} describes $X_{\alpha,t}$ as a boundary-crossing rate and not as a measure of
788	interdisciplinarity in the sense of \citet{stirling2007diversity} or \citet{rafols2010diversity}, whose	790	interdisciplinarity in the sense of \citet{stirling2007diversity} or \citet{rafols2010diversity}, whose
789	−indicators weight pairs of categories by their distance. The security, software, theory and data areas	791	+indicators weight pairs of categories by their distance. The security, software and data areas
790	−rise through the whole window at a steady pace, the hardware area is flat, computational science rises and	792	+rise through the whole window; the theory area rises in the sparse early years, is flat in the late
		793	+baseline and rises again in the post period; the hardware area is flat, computational science rises and
791	falls back in the incomplete year, and the perception area, after a fall in 2019 that belongs to the small	794	falls back in the incomplete year, and the perception area, after a fall in 2019 that belongs to the small
792	2018 network, is flat at the lowest level. Table~\ref{tab:net_modularity} aggregates these shares over areas	795	2018 network, is flat at the lowest level. Table~\ref{tab:net_modularity} aggregates these shares over areas
793	in two ways. The raw aggregate is flat through the baseline and rises in every post-period year; the	796	in two ways. The raw aggregate is flat through the baseline and rises in every post-period year; the
				`@@ -800,14 +803,15 @@ series, with the composition qualification stated.`
800		803
801	H4's second component is the modularity. $Q_{\mathrm{cat}}$ peaks in 2019 and falls in every subsequent	804	H4's second component is the modularity. $Q_{\mathrm{cat}}$ peaks in 2019 and falls in every subsequent
802	year; $Q_{\mathrm{area}}$ falls in every year from 2018 to 2025 and is flat, with a slight uptick in the	805	year; $Q_{\mathrm{area}}$ falls in every year from 2018 to 2025 and is flat, with a slight uptick in the
803	−incomplete year, from 2023 onward; the self-share peaks in 2019 and falls thereafter, fastest after 2023	806	+incomplete year, from 2023 onward; the self-share peaks in 2019, is nearly flat through 2022 and falls in every post-period year, fastest after 2023
804	(Table~\ref{tab:net_modularity}, Figure~\ref{fig:cross_area}, right). The post-period means are below the	807	(Table~\ref{tab:net_modularity}, Figure~\ref{fig:cross_area}, right). The post-period means are below the
805	baseline means at both partitions, as the note of the table records, so the verdict on this component is	808	baseline means at both partitions, as the note of the table records, so the verdict on this component is
806	that the hypothesis is supported in direction at both levels. The caveats are substantive. The decline is	809	that the hypothesis is supported in direction at both levels. The caveats are substantive. The decline is
807	−present throughout the baseline; the 2022 boundary is not a break in either series, and the yearly	810	+present throughout the baseline; the 2022 boundary is not a break in either series; the yearly
808	−decrements of $Q_{\mathrm{cat}}$ in the post period are of the same order as the larger baseline	811	+decrements of $Q_{\mathrm{cat}}$ are somewhat larger in the post period than in the late baseline, while
809	−decrements, while $Q_{\mathrm{area}}$ stops falling in the post period, so that at the area partition the	812	+$Q_{\mathrm{area}}$ stops falling after 2023, so that the two partitions tell different stories about the
810	−post period is where the decline ends rather than where it begins. Because the partition is fixed, a fall in	813	+post period: continued, slightly faster de-assortment between categories, and an end to the decline between
		814	+areas. Because the partition is fixed, a fall in
811	$Q$ can arise from a change in the mix of citing works as well as from a change in citing behaviour; the	815	$Q$ can arise from a change in the mix of citing works as well as from a change in citing behaviour; the
812	adjusted cross-area series shows that both are present. The modularity of equation~\eqref{eq:modularity}	816	adjusted cross-area series shows that both are present. The modularity of equation~\eqref{eq:modularity}
813	inherits every property of the classifier, and in particular the placement of large language models in the	817	inherits every property of the classifier, and in particular the placement of large language models in the
				`@@ -889,7 +893,7 @@ names all send a growing share of their references to the AI/ML categories, with`
889	human--computer interaction and software engineering in 2024--2026 rather than at the boundary	893	human--computer interaction and software engineering in 2024--2026 rather than at the boundary
890	(Tables~\ref{tab:net_category_to_ai} and~\ref{tab:net_modularity}). The modularity falls at both the	894	(Tables~\ref{tab:net_category_to_ai} and~\ref{tab:net_modularity}). The modularity falls at both the
891	category and the area partition, with post-period means below baseline means, but the fall is a	895	category and the area partition, with post-period means below baseline means, but the fall is a
892	−baseline trend that continues at the category level and stops at the area level after 2023; part of it is	896	+baseline trend that continues, slightly faster, at the category level and stops at the area level after 2023; part of it is
893	the growing weight of the most boundary-crossing area among citing works, and all of it depends on where the	897	the growing weight of the most boundary-crossing area among citing works, and all of it depends on where the
894	ontology draws the boundary between machine learning and its application fields	898	ontology draws the boundary between machine learning and its application fields
895	(Section~\ref{sec:net-flows-categories}). The field-level counterpart, the share of computer-science	899	(Section~\ref{sec:net-flows-categories}). The field-level counterpart, the share of computer-science

modified manuscript/tables/TABLES.json +57 −57

				`@@ -103,7 +103,7 @@`
103	"caption": "Fractional output of the fifteen largest countries by year.",	103	"caption": "Fractional output of the fifteen largest countries by year.",
104	"label": "tab:countries",	104	"label": "tab:countries",
105	"sources": [	105	"sources": [
106	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv"	106	+ "./results/bibliometrics/countries_yearly.csv"
107	],	107	],
108	"rows": 15	108	"rows": 15
109	},	109	},
				`@@ -112,7 +112,7 @@`
112	"caption": "Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai\u2013Perron (BIC) and PELT break dates.",	112	"caption": "Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai\u2013Perron (BIC) and PELT break dates.",
113	"label": "tab:breaks",	113	"label": "tab:breaks",
114	"sources": [	114	"sources": [
115	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv"	115	+ "./results/econometrics/breaks.csv"
116	],	116	],
117	"rows": 41	117	"rows": 41
118	},	118	},
				`@@ -121,7 +121,7 @@`
121	"caption": "Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01\u20132024-10).",	121	"caption": "Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01\u20132024-10).",
122	"label": "tab:emerging_h12",	122	"label": "tab:emerging_h12",
123	"sources": [	123	"sources": [
124	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h12.json"	124	+ "./results/emerging/metrics_h12.json"
125	],	125	],
126	"rows": 3	126	"rows": 3
127	},	127	},
				`@@ -130,7 +130,7 @@`
130	"caption": "Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01\u20132024-04).",	130	"caption": "Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01\u20132024-04).",
131	"label": "tab:emerging_h24",	131	"label": "tab:emerging_h24",
132	"sources": [	132	"sources": [
133	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h24.json"	133	+ "./results/emerging/metrics_h24.json"
134	],	134	],
135	"rows": 3	135	"rows": 3
136	},	136	},
				`@@ -139,7 +139,7 @@`
139	"caption": "Preregistered known-emergence set: detection month, peak-growth month and lead time.",	139	"caption": "Preregistered known-emergence set: detection month, peak-growth month and lead time.",
140	"label": "tab:known_set",	140	"label": "tab:known_set",
141	"sources": [	141	"sources": [
142	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/known_set_lead_times.csv"	142	+ "./results/emerging/known_set_lead_times.csv"
143	],	143	],
144	"rows": 12	144	"rows": 12
145	},	145	},
				`@@ -148,8 +148,8 @@`
148	"caption": "Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).",	148	"caption": "Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).",
149	"label": "tab:collaboration",	149	"label": "tab:collaboration",
150	"sources": [	150	"sources": [
151	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/team_size_yearly.csv",	151	+ "./results/bibliometrics/team_size_yearly.csv",
152	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/international_collab_yearly.csv"	152	+ "./results/bibliometrics/international_collab_yearly.csv"
153	],	153	],
154	"rows": 9	154	"rows": 9
155	},	155	},
				`@@ -158,7 +158,7 @@`
158	"caption": "Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).",	158	"caption": "Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).",
159	"label": "tab:institution_types",	159	"label": "tab:institution_types",
160	"sources": [	160	"sources": [
161	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/institution_types_yearly.csv"	161	+ "./results/bibliometrics/institution_types_yearly.csv"
162	],	162	],
163	"rows": 9	163	"rows": 9
164	},	164	},
				`@@ -167,7 +167,7 @@`
167	"caption": "Share of fractional output of the fifteen largest countries by year (per cent).",	167	"caption": "Share of fractional output of the fifteen largest countries by year (per cent).",
168	"label": "tab:country_shares",	168	"label": "tab:country_shares",
169	"sources": [	169	"sources": [
170	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv"	170	+ "./results/bibliometrics/countries_yearly.csv"
171	],	171	],
172	"rows": 15	172	"rows": 15
173	},	173	},
				`@@ -176,7 +176,7 @@`
176	"caption": "Concentration of output across countries by year, and the two largest producers under full counting.",	176	"caption": "Concentration of output across countries by year, and the two largest producers under full counting.",
177	"label": "tab:country_concentration",	177	"label": "tab:country_concentration",
178	"sources": [	178	"sources": [
179	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv"	179	+ "./results/bibliometrics/countries_yearly.csv"
180	],	180	],
181	"rows": 9	181	"rows": 9
182	},	182	},
				`@@ -185,7 +185,7 @@`
185	"caption": "The country co-authorship network by period: edges with at least fifty joint works.",	185	"caption": "The country co-authorship network by period: edges with at least fifty joint works.",
186	"label": "tab:country_network",	186	"label": "tab:country_network",
187	"sources": [	187	"sources": [
188	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv"	188	+ "./results/bibliometrics/country_pairs_by_period.csv"
189	],	189	],
190	"rows": 2	190	"rows": 2
191	},	191	},
				`@@ -194,7 +194,7 @@`
194	"caption": "The twenty strongest country pairs by period (number of joint works).",	194	"caption": "The twenty strongest country pairs by period (number of joint works).",
195	"label": "tab:country_pairs",	195	"label": "tab:country_pairs",
196	"sources": [	196	"sources": [
197	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv"	197	+ "./results/bibliometrics/country_pairs_by_period.csv"
198	],	198	],
199	"rows": 20	199	"rows": 20
200	},	200	},
				`@@ -203,7 +203,7 @@`
203	"caption": "Institutions with at least two hundred works per period: coverage and concentration (fractional counting).",	203	"caption": "Institutions with at least two hundred works per period: coverage and concentration (fractional counting).",
204	"label": "tab:institution_concentration",	204	"label": "tab:institution_concentration",
205	"sources": [	205	"sources": [
206	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv"	206	+ "./results/bibliometrics/top_institutions_by_period.csv"
207	],	207	],
208	"rows": 3	208	"rows": 3
209	},	209	},
				`@@ -212,7 +212,7 @@`
212	"caption": "The twenty-five institutions with the largest fractional output in the post period, with their baseline output.",	212	"caption": "The twenty-five institutions with the largest fractional output in the post period, with their baseline output.",
213	"label": "tab:top_institutions",	213	"label": "tab:top_institutions",
214	"sources": [	214	"sources": [
215	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv"	215	+ "./results/bibliometrics/top_institutions_by_period.csv"
216	],	216	],
217	"rows": 25	217	"rows": 25
218	},	218	},
				`@@ -221,7 +221,7 @@`
221	"caption": "Distribution of works over source types among the listed sources, by period (per cent).",	221	"caption": "Distribution of works over source types among the listed sources, by period (per cent).",
222	"label": "tab:source_types",	222	"label": "tab:source_types",
223	"sources": [	223	"sources": [
224	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv"	224	+ "./results/bibliometrics/top_sources_by_period.csv"
225	],	225	],
226	"rows": 2	226	"rows": 2
227	},	227	},
				`@@ -230,7 +230,7 @@`
230	"caption": "The twenty largest sources (venues) by period, with OpenAlex source type.",	230	"caption": "The twenty largest sources (venues) by period, with OpenAlex source type.",
231	"label": "tab:top_sources",	231	"label": "tab:top_sources",
232	"sources": [	232	"sources": [
233	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv"	233	+ "./results/bibliometrics/top_sources_by_period.csv"
234	],	234	],
235	"rows": 20	235	"rows": 20
236	},	236	},
				`@@ -239,7 +239,7 @@`
239	"caption": "Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).",	239	"caption": "Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).",
240	"label": "tab:cited_age",	240	"label": "tab:cited_age",
241	"sources": [	241	"sources": [
242	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/cited_age_yearly.csv"	242	+ "./results/graph/cited_age_yearly.csv"
243	],	243	],
244	"rows": 9	244	"rows": 9
245	},	245	},
				`@@ -248,7 +248,7 @@`
248	"caption": "The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.",	248	"caption": "The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.",
249	"label": "tab:top_cited",	249	"label": "tab:top_cited",
250	"sources": [	250	"sources": [
251	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_cited_by_year.csv"	251	+ "./results/bibliometrics/top_cited_by_year.csv"
252	],	252	],
253	"rows": 80	253	"rows": 80
254	},	254	},
				`@@ -257,7 +257,7 @@`
257	"caption": "DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.",	257	"caption": "DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.",
258	"label": "tab:dblp_only",	258	"label": "tab:dblp_only",
259	"sources": [	259	"sources": [
260	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/dblp_only_yearly.csv"	260	+ "./results/bibliometrics/dblp_only_yearly.csv"
261	],	261	],
262	"rows": 9	262	"rows": 9
263	},	263	},
				`@@ -266,7 +266,7 @@`
266	"caption": "The OpenAlex \\texttt{created\\_date} relative to the publication date, by publication year.",	266	"caption": "The OpenAlex \\texttt{created\\_date} relative to the publication date, by publication year.",
267	"label": "tab:indexing_lag",	267	"label": "tab:indexing_lag",
268	"sources": [	268	"sources": [
269	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/indexing_lag.csv"	269	+ "./results/bibliometrics/indexing_lag.csv"
270	],	270	],
271	"rows": 9	271	"rows": 9
272	},	272	},
				`@@ -275,7 +275,7 @@`
275	"caption": "Reference coverage by citing year: declared, resolved, internal and external references of eligible works.",	275	"caption": "Reference coverage by citing year: declared, resolved, internal and external references of eligible works.",
276	"label": "tab:reference_coverage",	276	"label": "tab:reference_coverage",
277	"sources": [	277	"sources": [
278	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/reference_coverage_yearly.csv"	278	+ "./results/graph/reference_coverage_yearly.csv"
279	],	279	],
280	"rows": 9	280	"rows": 9
281	},	281	},
				`@@ -284,7 +284,7 @@`
284	"caption": "Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).",	284	"caption": "Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).",
285	"label": "tab:field_flows",	285	"label": "tab:field_flows",
286	"sources": [	286	"sources": [
287	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/field_flows_yearly.csv"	287	+ "./results/graph/field_flows_yearly.csv"
288	],	288	],
289	"rows": 9	289	"rows": 9
290	},	290	},
				`@@ -293,7 +293,7 @@`
293	"caption": "Citation-flow matrices between the eight areas of the ontology by period: share of each citing area's internal references received by each cited area (per cent).",	293	"caption": "Citation-flow matrices between the eight areas of the ontology by period: share of each citing area's internal references received by each cited area (per cent).",
294	"label": "tab:net_area_flows",	294	"label": "tab:net_area_flows",
295	"sources": [	295	"sources": [
296	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/area_flows_yearly.csv"	296	+ "./results/graph/area_flows_yearly.csv"
297	],	297	],
298	"rows": 16	298	"rows": 16
299	},	299	},
				`@@ -302,8 +302,8 @@`
302	"caption": "Modularity of the yearly category citation-flow network at the category and area partitions, the within-category self-share, and the aggregate cross-area share of internal references, raw and composition-adjusted.",	302	"caption": "Modularity of the yearly category citation-flow network at the category and area partitions, the within-category self-share, and the aggregate cross-area share of internal references, raw and composition-adjusted.",
303	"label": "tab:net_modularity",	303	"label": "tab:net_modularity",
304	"sources": [	304	"sources": [
305	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/modularity_yearly.csv",	305	+ "./results/graph/modularity_yearly.csv",
306	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/cross_area_share_yearly.csv"	306	+ "./results/graph/cross_area_share_yearly.csv"
307	],	307	],
308	"rows": 9	308	"rows": 9
309	},	309	},
				`@@ -312,7 +312,7 @@`
312	"caption": "Share of each category's internal references received by the nine categories of the AI/ML area, by citing year (per cent).",	312	"caption": "Share of each category's internal references received by the nine categories of the AI/ML area, by citing year (per cent).",
313	"label": "tab:net_category_to_ai",	313	"label": "tab:net_category_to_ai",
314	"sources": [	314	"sources": [
315	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_flows_yearly.csv"	315	+ "./results/graph/category_flows_yearly.csv"
316	],	316	],
317	"rows": 40	317	"rows": 40
318	},	318	},
				`@@ -321,7 +321,7 @@`
321	"caption": "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).",	321	"caption": "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).",
322	"label": "tab:net_category_actors",	322	"label": "tab:net_category_actors",
323	"sources": [	323	"sources": [
324	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_actors_yearly.csv"	324	+ "./results/bibliometrics/category_actors_yearly.csv"
325	],	325	],
326	"rows": 40	326	"rows": 40
327	},	327	},
				`@@ -330,7 +330,7 @@`
330	"caption": "Actor shares of works whose primary category is in the AI/ML area against all other areas, by period (per cent).",	330	"caption": "Actor shares of works whose primary category is in the AI/ML area against all other areas, by period (per cent).",
331	"label": "tab:net_ai_vs_rest",	331	"label": "tab:net_ai_vs_rest",
332	"sources": [	332	"sources": [
333	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_actors_yearly.csv"	333	+ "./results/bibliometrics/category_actors_yearly.csv"
334	],	334	],
335	"rows": 4	335	"rows": 4
336	},	336	},
				`@@ -339,7 +339,7 @@`
339	"caption": "China-affiliated, company-affiliated and international shares of works by year, AI/ML area against all other areas (per cent).",	339	"caption": "China-affiliated, company-affiliated and international shares of works by year, AI/ML area against all other areas (per cent).",
340	"label": "tab:net_ai_vs_rest_yearly",	340	"label": "tab:net_ai_vs_rest_yearly",
341	"sources": [	341	"sources": [
342	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_actors_yearly.csv"	342	+ "./results/bibliometrics/category_actors_yearly.csv"
343	],	343	],
344	"rows": 9	344	"rows": 9
345	},	345	},
				`@@ -348,16 +348,16 @@`
348	"caption": "Gini coefficient of 36-month citations $c^{(36)}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.",	348	"caption": "Gini coefficient of 36-month citations $c^{(36)}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.",
349	"label": "tab:net_category_gini",	349	"label": "tab:net_category_gini",
350	"sources": [	350	"sources": [
351	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_impact_yearly.csv"	351	+ "./results/bibliometrics/category_impact_yearly.csv"
352	],	352	],
353	"rows": 40	353	"rows": 40
354	},	354	},
355	{	355	{
356	"file": "tab_net_gini_ai_vs_rest.tex",	356	"file": "tab_net_gini_ai_vs_rest.tex",
357	− "caption": "Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.",	357	+ "caption": "Citation inequality at 36 months, AI/ML area against all other areas, by publication year with a complete window.",
358	"label": "tab:net_gini_ai_vs_rest",	358	"label": "tab:net_gini_ai_vs_rest",
359	"sources": [	359	"sources": [
360	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_impact_yearly.csv"	360	+ "./results/bibliometrics/category_impact_yearly.csv"
361	],	361	],
362	"rows": 6	362	"rows": 6
363	},	363	},
				`@@ -366,7 +366,7 @@`
366	"caption": "Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.",	366	"caption": "Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.",
367	"label": "tab:ai_growth",	367	"label": "tab:ai_growth",
368	"sources": [	368	"sources": [
369	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv"	369	+ "./results/bibliometrics/cagr_by_category.csv"
370	],	370	],
371	"rows": 9	371	"rows": 9
372	},	372	},
				`@@ -375,7 +375,7 @@`
375	"caption": "Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).",	375	"caption": "Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).",
376	"label": "tab:ai_counts",	376	"label": "tab:ai_counts",
377	"sources": [	377	"sources": [
378	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv"	378	+ "./results/graph/category_counts_yearly.csv"
379	],	379	],
380	"rows": 9	380	"rows": 9
381	},	381	},
				`@@ -384,7 +384,7 @@`
384	"caption": "Structural change at 30 November 2022 in the nine AI categories: interrupted-time-series level and slope changes, placebo p-values, Bai--Perron and PELT break dates.",	384	"caption": "Structural change at 30 November 2022 in the nine AI categories: interrupted-time-series level and slope changes, placebo p-values, Bai--Perron and PELT break dates.",
385	"label": "tab:ai_breaks",	385	"label": "tab:ai_breaks",
386	"sources": [	386	"sources": [
387	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv"	387	+ "./results/econometrics/breaks.csv"
388	],	388	],
389	"rows": 9	389	"rows": 9
390	},	390	},
				`@@ -393,7 +393,7 @@`
393	"caption": "Interrupted-time-series level changes of the nine AI categories at the alternative event dates (GPT-3, DALL-E 2, GPT-4, o1).",	393	"caption": "Interrupted-time-series level changes of the nine AI categories at the alternative event dates (GPT-3, DALL-E 2, GPT-4, o1).",
394	"label": "tab:ai_altdates",	394	"label": "tab:ai_altdates",
395	"sources": [	395	"sources": [
396	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv"	396	+ "./results/econometrics/breaks.csv"
397	],	397	],
398	"rows": 9	398	"rows": 9
399	},	399	},
				`@@ -402,7 +402,7 @@`
402	"caption": "Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).",	402	"caption": "Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).",
403	"label": "tab:ai_flows",	403	"label": "tab:ai_flows",
404	"sources": [	404	"sources": [
405	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_flows_yearly.csv"	405	+ "./results/graph/category_flows_yearly.csv"
406	],	406	],
407	"rows": 27	407	"rows": 27
408	},	408	},
				`@@ -411,7 +411,7 @@`
411	"caption": "Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.",	411	"caption": "Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.",
412	"label": "tab:ai_landmark_flags",	412	"label": "tab:ai_landmark_flags",
413	"sources": [	413	"sources": [
414	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv"	414	+ "./literature_synthesis/agent_E/contributions.csv"
415	],	415	],
416	"rows": 9	416	"rows": 9
417	},	417	},
				`@@ -420,7 +420,7 @@`
420	"caption": "Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).",	420	"caption": "Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).",
421	"label": "tab:ai_contribution_types",	421	"label": "tab:ai_contribution_types",
422	"sources": [	422	"sources": [
423	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv"	423	+ "./literature_synthesis/agent_E/contributions.csv"
424	],	424	],
425	"rows": 9	425	"rows": 9
426	},	426	},
				`@@ -429,7 +429,7 @@`
429	"caption": "The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature\\_synthesis/agent\\_E/contributions.csv).",	429	"caption": "The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature\\_synthesis/agent\\_E/contributions.csv).",
430	"label": "tab:ai_landmarks",	430	"label": "tab:ai_landmarks",
431	"sources": [	431	"sources": [
432	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv"	432	+ "./literature_synthesis/agent_E/contributions.csv"
433	],	433	],
434	"rows": 234	434	"rows": 234
435	},	435	},
				`@@ -438,7 +438,7 @@`
438	"caption": "Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).",	438	"caption": "Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).",
439	"label": "tab:security_counts",	439	"label": "tab:security_counts",
440	"sources": [	440	"sources": [
441	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv"	441	+ "./results/graph/category_counts_yearly.csv"
442	],	442	],
443	"rows": 9	443	"rows": 9
444	},	444	},
				`@@ -447,8 +447,8 @@`
447	"caption": "Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).",	447	"caption": "Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).",
448	"label": "tab:security_summary",	448	"label": "tab:security_summary",
449	"sources": [	449	"sources": [
450	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv",	450	+ "./results/graph/category_counts_yearly.csv",
451	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv"	451	+ "./results/econometrics/breaks.csv"
452	],	452	],
453	"rows": 5	453	"rows": 5
454	},	454	},
				`@@ -457,8 +457,8 @@`
457	"caption": "Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.",	457	"caption": "Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.",
458	"label": "tab:security_llm_cooccurrence",	458	"label": "tab:security_llm_cooccurrence",
459	"sources": [	459	"sources": [
460	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv",	460	+ "./results/graph/category_cooccurrence_yearly.csv",
461	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv"	461	+ "./results/graph/category_counts_yearly.csv"
462	],	462	],
463	"rows": 9	463	"rows": 9
464	},	464	},
				`@@ -467,7 +467,7 @@`
467	"caption": "Composition of the automatically selected landmark set of the security-and-privacy area, by category.",	467	"caption": "Composition of the automatically selected landmark set of the security-and-privacy area, by category.",
468	"label": "tab:security_landmarks",	468	"label": "tab:security_landmarks",
469	"sources": [	469	"sources": [
470	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/security_privacy_landmarks.csv"	470	+ "./literature_synthesis/landmarks/security_privacy_landmarks.csv"
471	],	471	],
472	"rows": 5	472	"rows": 5
473	},	473	},
				`@@ -476,8 +476,8 @@`
476	"caption": "The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.",	476	"caption": "The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.",
477	"label": "tab:systems_categories",	477	"label": "tab:systems_categories",
478	"sources": [	478	"sources": [
479	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv",	479	+ "./results/bibliometrics/cagr_by_category.csv",
480	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv"	480	+ "./results/econometrics/breaks.csv"
481	],	481	],
482	"rows": 11	482	"rows": 11
483	},	483	},
				`@@ -486,7 +486,7 @@`
486	"caption": "Eligible works in the six theory and software categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).",	486	"caption": "Eligible works in the six theory and software categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).",
487	"label": "tab:theory_counts",	487	"label": "tab:theory_counts",
488	"sources": [	488	"sources": [
489	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv"	489	+ "./results/graph/category_counts_yearly.csv"
490	],	490	],
491	"rows": 9	491	"rows": 9
492	},	492	},
				`@@ -495,8 +495,8 @@`
495	"caption": "Growth and structural-change summary for the six theory and software categories (multi-label counts).",	495	"caption": "Growth and structural-change summary for the six theory and software categories (multi-label counts).",
496	"label": "tab:theory_summary",	496	"label": "tab:theory_summary",
497	"sources": [	497	"sources": [
498	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv",	498	+ "./results/graph/category_counts_yearly.csv",
499	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv"	499	+ "./results/econometrics/breaks.csv"
500	],	500	],
501	"rows": 6	501	"rows": 6
502	},	502	},
				`@@ -505,8 +505,8 @@`
505	"caption": "Co-classification of theory and software works with the LLM category, by publication year: number of works carrying both categories and share of the category's multi-label count.",	505	"caption": "Co-classification of theory and software works with the LLM category, by publication year: number of works carrying both categories and share of the category's multi-label count.",
506	"label": "tab:theory_llm_cooccurrence",	506	"label": "tab:theory_llm_cooccurrence",
507	"sources": [	507	"sources": [
508	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv",	508	+ "./results/graph/category_cooccurrence_yearly.csv",
509	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv"	509	+ "./results/graph/category_counts_yearly.csv"
510	],	510	],
511	"rows": 9	511	"rows": 9
512	},	512	},
				`@@ -515,9 +515,9 @@`
515	"caption": "Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.",	515	"caption": "Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.",
516	"label": "tab:theory_landmark_audit",	516	"label": "tab:theory_landmark_audit",
517	"sources": [	517	"sources": [
518	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_G/contributions.csv",	518	+ "./literature_synthesis/agent_G/contributions.csv",
519	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/theory_landmarks.csv",	519	+ "./literature_synthesis/landmarks/theory_landmarks.csv",
520	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/software_pl_landmarks.csv"	520	+ "./literature_synthesis/landmarks/software_pl_landmarks.csv"
521	],	521	],
522	"rows": 9	522	"rows": 9
523	},	523	},
				`@@ -526,7 +526,7 @@`
526	"caption": "Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.",	526	"caption": "Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.",
527	"label": "tab:theory_landmarks_discussed",	527	"label": "tab:theory_landmarks_discussed",
528	"sources": [	528	"sources": [
529	− "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_G/contributions.csv"	529	+ "./literature_synthesis/agent_G/contributions.csv"
530	],	530	],
531	"rows": 37	531	"rows": 37
532	}	532	}

modified manuscript/tables/tab_ai_altdates.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv	1	+% generated by tables/make_tables.py from ./results/econometrics/breaks.csv
2	\begin{table}[htbp]\centering\caption{Interrupted-time-series level changes of the nine AI categories at the alternative event dates (GPT-3, DALL-E 2, GPT-4, o1).}\label{tab:ai_altdates}	2	\begin{table}[htbp]\centering\caption{Interrupted-time-series level changes of the nine AI categories at the alternative event dates (GPT-3, DALL-E 2, GPT-4, o1).}\label{tab:ai_altdates}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrr}	4	\begin{tabular}{lrrrrrrrr}

modified manuscript/tables/tab_ai_breaks.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv	1	+% generated by tables/make_tables.py from ./results/econometrics/breaks.csv
2	\begin{table}[htbp]\centering\caption{Structural change at 30 November 2022 in the nine AI categories: interrupted-time-series level and slope changes, placebo p-values, Bai--Perron and PELT break dates.}\label{tab:ai_breaks}	2	\begin{table}[htbp]\centering\caption{Structural change at 30 November 2022 in the nine AI categories: interrupted-time-series level and slope changes, placebo p-values, Bai--Perron and PELT break dates.}\label{tab:ai_breaks}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrr}	4	\begin{tabular}{lrrrrrrrr}

modified manuscript/tables/tab_ai_contribution_types.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv	1	+% generated by tables/make_tables.py from ./literature_synthesis/agent_E/contributions.csv
2	\begin{table}[htbp]\centering\caption{Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).}\label{tab:ai_contribution_types}	2	\begin{table}[htbp]\centering\caption{Contribution type of the 234 AI/ML landmark candidates by primary category (specialist judgement from abstract or metadata).}\label{tab:ai_contribution_types}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrr}	4	\begin{tabular}{lrrrrrrr}

modified manuscript/tables/tab_ai_counts.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv
2	\begin{table}[htbp]\centering\caption{Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).}\label{tab:ai_counts}	2	\begin{table}[htbp]\centering\caption{Yearly multi-label counts of the nine AI categories (all period rows summed; 2026 incomplete).}\label{tab:ai_counts}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_ai_flows.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_flows_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_flows_yearly.csv
2	\begin{longtable}{lrrrrrrrrr}	2	\begin{longtable}{lrrrrrrrrr}
3	\caption{Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).}\label{tab:ai_flows}\\	3	\caption{Destination of the in-corpus references of the nine AI categories in 2019, 2022 and 2025 (per cent of in-corpus references of the citing category).}\label{tab:ai_flows}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_ai_growth.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/cagr_by_category.csv
2	\begin{table}[htbp]\centering\caption{Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.}\label{tab:ai_growth}	2	\begin{table}[htbp]\centering\caption{Volume, compound annual growth and corpus share of the nine AI categories, baseline (2018--2022) and post (2023--2025) periods.}\label{tab:ai_growth}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_ai_landmark_flags.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv	1	+% generated by tables/make_tables.py from ./literature_synthesis/agent_E/contributions.csv
2	\begin{table}[htbp]\centering\caption{Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.}\label{tab:ai_landmark_flags}	2	\begin{table}[htbp]\centering\caption{Specialist screening of the 234 AI/ML landmark candidates: records by primary category and data-quality flag.}\label{tab:ai_landmark_flags}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrr}	4	\begin{tabular}{lrrrrrrrr}

modified manuscript/tables/tab_ai_landmarks.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_E/contributions.csv	1	+% generated by tables/make_tables.py from ./literature_synthesis/agent_E/contributions.csv
2	\begin{longtable}{lrrrrrrr}	2	\begin{longtable}{lrrrrrrr}
3	\caption{The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature\_synthesis/agent\_E/contributions.csv).}\label{tab:ai_landmarks}\\	3	\caption{The 234 AI/ML landmark candidates with the specialist's contribution type and data-quality flag (titles in literature\_synthesis/agent\_E/contributions.csv).}\label{tab:ai_landmarks}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_breaks.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv	1	+% generated by tables/make_tables.py from ./results/econometrics/breaks.csv
2	\begin{longtable}{lrrrrrrrr}	2	\begin{longtable}{lrrrrrrrr}
3	\caption{Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai–Perron (BIC) and PELT break dates.}\label{tab:breaks}\\	3	\caption{Structural-change estimates by series: ITS level and slope changes at 2022-12 (HAC p-values), placebo p-values, Bai–Perron (BIC) and PELT break dates.}\label{tab:breaks}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_cited_age.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/cited_age_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/cited_age_yearly.csv
2	\begin{table}[htbp]\centering\caption{Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).}\label{tab:cited_age}	2	\begin{table}[htbp]\centering\caption{Age of cited works by citing year (resolved references of eligible works; corpus version of the result file at generation time).}\label{tab:cited_age}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrr}	4	\begin{tabular}{lrrrrrrr}

modified manuscript/tables/tab_collaboration.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/team_size_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/international_collab_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/team_size_yearly.csv, ./results/bibliometrics/international_collab_yearly.csv
2	\begin{table}[htbp]\centering\caption{Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).}\label{tab:collaboration}	2	\begin{table}[htbp]\centering\caption{Team size and collaboration indicators by publication year (all eligible works; periods of the source file summed).}\label{tab:collaboration}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrr}	4	\begin{tabular}{lrrrrrrrr}

modified manuscript/tables/tab_countries.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/countries_yearly.csv
2	\begin{table}[htbp]\centering\caption{Fractional output of the fifteen largest countries by year.}\label{tab:countries}	2	\begin{table}[htbp]\centering\caption{Fractional output of the fifteen largest countries by year.}\label{tab:countries}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_country_concentration.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/countries_yearly.csv
2	\begin{table}[htbp]\centering\caption{Concentration of output across countries by year, and the two largest producers under full counting.}\label{tab:country_concentration}	2	\begin{table}[htbp]\centering\caption{Concentration of output across countries by year, and the two largest producers under full counting.}\label{tab:country_concentration}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_country_network.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/country_pairs_by_period.csv
2	\begin{table}[htbp]\centering\caption{The country co-authorship network by period: edges with at least fifty joint works.}\label{tab:country_network}	2	\begin{table}[htbp]\centering\caption{The country co-authorship network by period: edges with at least fifty joint works.}\label{tab:country_network}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrr}	4	\begin{tabular}{lrrrrrr}

modified manuscript/tables/tab_country_pairs.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/country_pairs_by_period.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/country_pairs_by_period.csv
2	\begin{table}[htbp]\centering\caption{The twenty strongest country pairs by period (number of joint works).}\label{tab:country_pairs}	2	\begin{table}[htbp]\centering\caption{The twenty strongest country pairs by period (number of joint works).}\label{tab:country_pairs}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrr}	4	\begin{tabular}{lrrrr}

modified manuscript/tables/tab_country_shares.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/countries_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/countries_yearly.csv
2	\begin{table}[htbp]\centering\caption{Share of fractional output of the fifteen largest countries by year (per cent).}\label{tab:country_shares}	2	\begin{table}[htbp]\centering\caption{Share of fractional output of the fifteen largest countries by year (per cent).}\label{tab:country_shares}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_dblp_only.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/dblp_only_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/dblp_only_yearly.csv
2	\begin{table}[htbp]\centering\caption{DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.}\label{tab:dblp_only}	2	\begin{table}[htbp]\centering\caption{DBLP records of 2018--2026 without an OpenAlex counterpart, by DBLP record type and year.}\label{tab:dblp_only}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrr}	4	\begin{tabular}{lrrrrrrrr}

modified manuscript/tables/tab_emerging_h12.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h12.json	1	+% generated by tables/make_tables.py from ./results/emerging/metrics_h12.json
2	\begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01–2024-10).}\label{tab:emerging_h12}	2	\begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 12 months (test cutoffs 2023-01–2024-10).}\label{tab:emerging_h12}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrr}	4	\begin{tabular}{lrrr}

modified manuscript/tables/tab_emerging_h24.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/metrics_h24.json	1	+% generated by tables/make_tables.py from ./results/emerging/metrics_h24.json
2	\begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01–2024-04).}\label{tab:emerging_h24}	2	\begin{table}[htbp]\centering\caption{Out-of-time performance of the emerging-topic detectors at horizon 24 months (test cutoffs 2023-01–2024-04).}\label{tab:emerging_h24}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrr}	4	\begin{tabular}{lrrr}

modified manuscript/tables/tab_field_flows.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/field_flows_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/field_flows_yearly.csv
2	\begin{table}[htbp]\centering\caption{Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).}\label{tab:field_flows}	2	\begin{table}[htbp]\centering\caption{Destination of the resolved references of eligible works by citing year: inside the corpus, computer science outside it, and the seven largest other OpenAlex fields (per cent of resolved references).}\label{tab:field_flows}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrrr}

modified manuscript/tables/tab_indexing_lag.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/indexing_lag.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/indexing_lag.csv
2	\begin{table}[htbp]\centering\caption{The OpenAlex \texttt{created\_date} relative to the publication date, by publication year.}\label{tab:indexing_lag}	2	\begin{table}[htbp]\centering\caption{The OpenAlex \texttt{created\_date} relative to the publication date, by publication year.}\label{tab:indexing_lag}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrr}	4	\begin{tabular}{lrrrrr}

modified manuscript/tables/tab_institution_concentration.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/top_institutions_by_period.csv
2	\begin{table}[htbp]\centering\caption{Institutions with at least two hundred works per period: coverage and concentration (fractional counting).}\label{tab:institution_concentration}	2	\begin{table}[htbp]\centering\caption{Institutions with at least two hundred works per period: coverage and concentration (fractional counting).}\label{tab:institution_concentration}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrr}	4	\begin{tabular}{lrrrrrr}

modified manuscript/tables/tab_institution_types.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/institution_types_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/institution_types_yearly.csv
2	\begin{table}[htbp]\centering\caption{Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).}\label{tab:institution_types}	2	\begin{table}[htbp]\centering\caption{Share of typed affiliations by OpenAlex institution type and year (per cent of affiliation-work pairs).}\label{tab:institution_types}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_known_set.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/emerging/known_set_lead_times.csv	1	+% generated by tables/make_tables.py from ./results/emerging/known_set_lead_times.csv
2	\begin{table}[htbp]\centering\caption{Preregistered known-emergence set: detection month, peak-growth month and lead time.}\label{tab:known_set}	2	\begin{table}[htbp]\centering\caption{Preregistered known-emergence set: detection month, peak-growth month and lead time.}\label{tab:known_set}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrr}	4	\begin{tabular}{lrrrr}

modified manuscript/tables/tab_net_ai_vs_rest.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_actors_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/category_actors_yearly.csv
2	\begin{table}[htbp]\centering\caption{Actor shares of works whose primary category is in the AI/ML area against all other areas, by period (per cent).}\label{tab:net_ai_vs_rest}	2	\begin{table}[htbp]\centering\caption{Actor shares of works whose primary category is in the AI/ML area against all other areas, by period (per cent).}\label{tab:net_ai_vs_rest}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrr}	4	\begin{tabular}{lrrrrrrrr}

modified manuscript/tables/tab_net_ai_vs_rest_yearly.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_actors_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/category_actors_yearly.csv
2	\begin{table}[htbp]\centering\caption{China-affiliated, company-affiliated and international shares of works by year, AI/ML area against all other areas (per cent).}\label{tab:net_ai_vs_rest_yearly}	2	\begin{table}[htbp]\centering\caption{China-affiliated, company-affiliated and international shares of works by year, AI/ML area against all other areas (per cent).}\label{tab:net_ai_vs_rest_yearly}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrr}	4	\begin{tabular}{lrrrrrr}

modified manuscript/tables/tab_net_area_flows.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/area_flows_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/area_flows_yearly.csv
2	\begin{table}[htbp]\centering\caption{Citation-flow matrices between the eight areas of the ontology by period: share of each citing area's internal references received by each cited area (per cent).}\label{tab:net_area_flows}	2	\begin{table}[htbp]\centering\caption{Citation-flow matrices between the eight areas of the ontology by period: share of each citing area's internal references received by each cited area (per cent).}\label{tab:net_area_flows}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_net_category_actors.tex +45 −45

				`@@ -1,54 +1,54 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_actors_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/category_actors_yearly.csv
2	−\begin{longtable}{lrrrrrrrrrr}	2	+\begin{longtable}{lrrrrrrrr}
3	\caption{Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).}\label{tab:net_category_actors}\\	3	\caption{Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).}\label{tab:net_category_actors}\\
4	\toprule	4	\toprule
5	−\textbf{Category} & \textbf{Works, base} & \textbf{Works, post} & \textbf{CN, base} & \textbf{CN, post} & \textbf{US, base} & \textbf{US, post} & \textbf{Comp., base} & \textbf{Comp., post} & \textbf{Intl., base} & \textbf{Intl., post} \\	5	+\textbf{Category} & \textbf{CN, base} & \textbf{CN, post} & \textbf{US, base} & \textbf{US, post} & \textbf{Comp., base} & \textbf{Comp., post} & \textbf{Intl., base} & \textbf{Intl., post} \\
6	\midrule	6	\midrule
7	\endfirsthead	7	\endfirsthead
8	\toprule	8	\toprule
9	−\textbf{Category} & \textbf{Works, base} & \textbf{Works, post} & \textbf{CN, base} & \textbf{CN, post} & \textbf{US, base} & \textbf{US, post} & \textbf{Comp., base} & \textbf{Comp., post} & \textbf{Intl., base} & \textbf{Intl., post} \\	9	+\textbf{Category} & \textbf{CN, base} & \textbf{CN, post} & \textbf{US, base} & \textbf{US, post} & \textbf{Comp., base} & \textbf{Comp., post} & \textbf{Intl., base} & \textbf{Intl., post} \\
10	\midrule	10	\midrule
11	\endhead	11	\endhead
12	−Large Language Models & 11{,}813 & 132{,}127 & 17.8 & 18.3 & 13.1 & 16.9 & 9.1 & 7.7 & 11.6 & 13.9 \\	12	+Large Language Models & 17.8 & 18.3 & 13.1 & 16.9 & 9.1 & 7.7 & 11.6 & 13.9 \\
13	−Foundation Models & 838 & 6{,}186 & 34.7 & 31.5 & 22.3 & 19.2 & 9.7 & 8.6 & 18.5 & 20.4 \\	13	+Foundation Models & 34.7 & 31.5 & 22.3 & 19.2 & 9.7 & 8.6 & 18.5 & 20.4 \\
14	−Multimodal AI & 4{,}007 & 20{,}970 & 44.7 & 47.2 & 17.5 & 12.8 & 12.8 & 8.6 & 20.9 & 19.2 \\	14	+Multimodal AI & 44.7 & 47.2 & 17.5 & 12.8 & 12.8 & 8.6 & 20.9 & 19.2 \\
15	−Generative AI & 7{,}503 & 38{,}194 & 25.8 & 22.1 & 18.2 & 12.1 & 7.8 & 3.5 & 16.1 & 13.2 \\	15	+Generative AI & 25.8 & 22.1 & 18.2 & 12.1 & 7.8 & 3.5 & 16.1 & 13.2 \\
16	−AI Agents and Autonomous Systems & 25{,}167 & 32{,}219 & 18.6 & 23.4 & 12.2 & 13.1 & 3.0 & 4.8 & 17.7 & 15.8 \\	16	+AI Agents and Autonomous Systems & 18.6 & 23.4 & 12.2 & 13.1 & 3.0 & 4.8 & 17.7 & 15.8 \\
17	−Reinforcement Learning & 26{,}913 & 44{,}477 & 21.9 & 33.8 & 24.4 & 15.9 & 10.1 & 6.3 & 16.9 & 16.3 \\	17	+Reinforcement Learning & 21.9 & 33.8 & 24.4 & 15.9 & 10.1 & 6.3 & 16.9 & 16.3 \\
18	−Machine Learning Theory & 88{,}923 & 112{,}830 & 16.3 & 18.8 & 22.4 & 16.2 & 7.5 & 4.8 & 18.0 & 16.9 \\	18	+Machine Learning Theory & 16.3 & 18.8 & 22.4 & 16.2 & 7.5 & 4.8 & 18.0 & 16.9 \\
19	−Deep Learning Architectures & 87{,}481 & 116{,}113 & 28.7 & 34.7 & 15.9 & 12.3 & 6.6 & 5.4 & 18.9 & 18.0 \\	19	+Deep Learning Architectures & 28.7 & 34.7 & 15.9 & 12.3 & 6.6 & 5.4 & 18.9 & 18.0 \\
20	−Computer Vision & 421{,}385 & 521{,}935 & 31.2 & 38.3 & 11.6 & 8.7 & 6.6 & 5.7 & 16.6 & 15.6 \\	20	+Computer Vision & 31.2 & 38.3 & 11.6 & 8.7 & 6.6 & 5.7 & 16.6 & 15.6 \\
21	−Natural Language Processing & 134{,}844 & 152{,}754 & 15.5 & 17.9 & 14.6 & 11.9 & 7.9 & 5.6 & 14.1 & 14.3 \\	21	+Natural Language Processing & 15.5 & 17.9 & 14.6 & 11.9 & 7.9 & 5.6 & 14.1 & 14.3 \\
22	−Speech and Audio Computing & 62{,}133 & 65{,}819 & 16.3 & 21.7 & 10.7 & 9.1 & 8.0 & 7.1 & 12.5 & 13.0 \\	22	+Speech and Audio Computing & 16.3 & 21.7 & 10.7 & 9.1 & 8.0 & 7.1 & 12.5 & 13.0 \\
23	−Robotics & 101{,}643 & 118{,}241 & 22.1 & 31.9 & 18.8 & 15.2 & 5.7 & 5.2 & 17.7 & 16.8 \\	23	+Robotics & 22.1 & 31.9 & 18.8 & 15.2 & 5.7 & 5.2 & 17.7 & 16.8 \\
24	−Human-Computer Interaction & 174{,}138 & 196{,}794 & 7.1 & 9.8 & 14.5 & 12.6 & 3.6 & 2.7 & 12.9 & 13.2 \\	24	+Human-Computer Interaction & 7.1 & 9.8 & 14.5 & 12.6 & 3.6 & 2.7 & 12.9 & 13.2 \\
25	−Software Engineering & 132{,}205 & 129{,}300 & 6.9 & 10.1 & 8.7 & 9.4 & 4.2 & 4.6 & 11.6 & 11.6 \\	25	+Software Engineering & 6.9 & 10.1 & 8.7 & 9.4 & 4.2 & 4.6 & 11.6 & 11.6 \\
26	−Programming Languages & 20{,}392 & 15{,}837 & 5.6 & 7.7 & 15.0 & 13.5 & 3.3 & 2.9 & 15.3 & 15.2 \\	26	+Programming Languages & 5.6 & 7.7 & 15.0 & 13.5 & 3.3 & 2.9 & 15.3 & 15.2 \\
27	−Compilers & 3{,}450 & 2{,}900 & 9.2 & 13.4 & 19.7 & 16.2 & 6.8 & 6.9 & 14.4 & 14.9 \\	27	+Compilers & 9.2 & 13.4 & 19.7 & 16.2 & 6.8 & 6.9 & 14.4 & 14.9 \\
28	−Formal Verification & 22{,}992 & 18{,}225 & 10.2 & 11.8 & 14.0 & 13.1 & 3.9 & 3.6 & 24.7 & 25.2 \\	28	+Formal Verification & 10.2 & 11.8 & 14.0 & 13.1 & 3.9 & 3.6 & 24.7 & 25.2 \\
29	−Algorithms and Data Structures & 144{,}105 & 121{,}771 & 19.2 & 24.6 & 15.6 & 13.9 & 3.1 & 3.1 & 23.2 & 22.2 \\	29	+Algorithms and Data Structures & 19.2 & 24.6 & 15.6 & 13.9 & 3.1 & 3.1 & 23.2 & 22.2 \\
30	−Computational Complexity & 18{,}778 & 15{,}760 & 10.9 & 13.2 & 14.3 & 11.6 & 1.8 & 1.8 & 19.1 & 18.9 \\	30	+Computational Complexity & 10.9 & 13.2 & 14.3 & 11.6 & 1.8 & 1.8 & 19.1 & 18.9 \\
31	−Database Systems & 74{,}552 & 61{,}582 & 13.1 & 14.6 & 10.7 & 9.2 & 4.4 & 5.1 & 13.4 & 11.8 \\	31	+Database Systems & 13.1 & 14.6 & 10.7 & 9.2 & 4.4 & 5.1 & 13.4 & 11.8 \\
32	−Information Retrieval & 58{,}198 & 61{,}738 & 22.3 & 29.0 & 13.3 & 10.7 & 8.1 & 9.0 & 16.1 & 14.8 \\	32	+Information Retrieval & 22.3 & 29.0 & 13.3 & 10.7 & 8.1 & 9.0 & 16.1 & 14.8 \\
33	−Distributed Computing & 52{,}661 & 43{,}642 & 19.5 & 25.3 & 18.9 & 16.1 & 5.7 & 6.7 & 17.9 & 17.0 \\	33	+Distributed Computing & 19.5 & 25.3 & 18.9 & 16.1 & 5.7 & 6.7 & 17.9 & 17.0 \\
34	−Cloud Computing & 40{,}415 & 36{,}757 & 15.0 & 13.9 & 8.8 & 9.6 & 4.9 & 6.8 & 13.2 & 11.0 \\	34	+Cloud Computing & 15.0 & 13.9 & 8.8 & 9.6 & 4.9 & 6.8 & 13.2 & 11.0 \\
35	−Edge Computing & 34{,}568 & 40{,}445 & 17.5 & 21.2 & 10.6 & 8.2 & 5.0 & 4.3 & 20.6 & 19.7 \\	35	+Edge Computing & 17.5 & 21.2 & 10.6 & 8.2 & 5.0 & 4.3 & 20.6 & 19.7 \\
36	−Computer Networks & 267{,}896 & 212{,}342 & 24.4 & 29.8 & 12.0 & 9.9 & 7.0 & 7.4 & 21.7 & 22.2 \\	36	+Computer Networks & 24.4 & 29.8 & 12.0 & 9.9 & 7.0 & 7.4 & 21.7 & 22.2 \\
37	−Cybersecurity & 154{,}197 & 195{,}498 & 12.3 & 14.0 & 12.9 & 11.1 & 3.9 & 3.7 & 12.9 & 12.9 \\	37	+Cybersecurity & 12.3 & 14.0 & 12.9 & 11.1 & 3.9 & 3.7 & 12.9 & 12.9 \\
38	−Applied Cryptography & 53{,}877 & 51{,}175 & 18.1 & 22.5 & 8.9 & 8.4 & 4.2 & 4.9 & 15.6 & 16.0 \\	38	+Applied Cryptography & 18.1 & 22.5 & 8.9 & 8.4 & 4.2 & 4.9 & 15.6 & 16.0 \\
39	−Blockchain Systems & 46{,}516 & 62{,}087 & 12.2 & 12.5 & 8.4 & 7.2 & 2.9 & 2.4 & 12.9 & 13.5 \\	39	+Blockchain Systems & 12.2 & 12.5 & 8.4 & 7.2 & 2.9 & 2.4 & 12.9 & 13.5 \\
40	−Computer Architecture & 86{,}471 & 70{,}512 & 13.8 & 22.2 & 20.0 & 17.8 & 9.9 & 9.8 & 15.8 & 15.1 \\	40	+Computer Architecture & 13.8 & 22.2 & 20.0 & 17.8 & 9.9 & 9.8 & 15.8 & 15.1 \\
41	−Accelerators and AI Hardware & 5{,}520 & 6{,}917 & 25.4 & 33.6 & 24.5 & 18.2 & 7.4 & 7.0 & 23.5 & 19.9 \\	41	+Accelerators and AI Hardware & 25.4 & 33.6 & 24.5 & 18.2 & 7.4 & 7.0 & 23.5 & 19.9 \\
42	−Operating Systems & 6{,}877 & 5{,}158 & 15.9 & 22.2 & 15.3 & 13.7 & 7.9 & 9.0 & 13.4 & 13.4 \\	42	+Operating Systems & 15.9 & 22.2 & 15.3 & 13.7 & 7.9 & 9.0 & 13.4 & 13.4 \\
43	−Quantum Computing & 39{,}846 & 60{,}417 & 19.3 & 16.0 & 18.2 & 19.8 & 6.9 & 8.4 & 21.3 & 20.1 \\	43	+Quantum Computing & 19.3 & 16.0 & 18.2 & 19.8 & 6.9 & 8.4 & 21.3 & 20.1 \\
44	−Scientific Computing & 93{,}944 & 84{,}877 & 24.8 & 27.9 & 17.4 & 16.2 & 3.4 & 3.4 & 24.0 & 22.9 \\	44	+Scientific Computing & 24.8 & 27.9 & 17.4 & 16.2 & 3.4 & 3.4 & 24.0 & 22.9 \\
45	−Computational Biology & 82{,}846 & 97{,}856 & 19.1 & 23.4 & 24.2 & 18.2 & 5.3 & 4.3 & 22.9 & 21.7 \\	45	+Computational Biology & 19.1 & 23.4 & 24.2 & 18.2 & 5.3 & 4.3 & 22.9 & 21.7 \\
46	−Computer Graphics & 46{,}021 & 43{,}270 & 14.2 & 20.6 & 14.0 & 11.9 & 5.1 & 5.4 & 13.4 & 12.6 \\	46	+Computer Graphics & 14.2 & 20.6 & 14.0 & 11.9 & 5.1 & 5.4 & 13.4 & 12.6 \\
47	−Privacy-Preserving Computation & 18{,}816 & 28{,}450 & 19.1 & 25.0 & 21.4 & 16.2 & 6.3 & 5.2 & 20.0 & 19.7 \\	47	+Privacy-Preserving Computation & 19.1 & 25.0 & 21.4 & 16.2 & 6.3 & 5.2 & 20.0 & 19.7 \\
48	−Federated Learning & 2{,}229 & 9{,}011 & 26.6 & 39.1 & 23.4 & 13.0 & 9.6 & 5.2 & 23.5 & 23.5 \\	48	+Federated Learning & 26.6 & 39.1 & 23.4 & 13.0 & 9.6 & 5.2 & 23.5 & 23.5 \\
49	−AI Safety and Alignment & 9{,}997 & 33{,}792 & 4.3 & 5.7 & 27.4 & 19.6 & 5.9 & 3.7 & 15.6 & 15.1 \\	49	+AI Safety and Alignment & 4.3 & 5.7 & 27.4 & 19.6 & 5.9 & 3.7 & 15.6 & 15.1 \\
50	−Sustainable Computing & 3{,}356 & 5{,}389 & 20.2 & 20.0 & 17.1 & 12.7 & 5.4 & 6.2 & 23.7 & 18.3 \\	50	+Sustainable Computing & 20.2 & 20.0 & 17.1 & 12.7 & 5.4 & 6.2 & 23.7 & 18.3 \\
51	−Autonomous Scientific Discovery & 841 & 2{,}539 & 8.1 & 14.6 & 33.4 & 23.1 & 8.7 & 6.7 & 18.5 & 18.2 \\	51	+Autonomous Scientific Discovery & 8.1 & 14.6 & 33.4 & 23.1 & 8.7 & 6.7 & 18.5 & 18.2 \\
52	\bottomrule	52	\bottomrule
53	−\multicolumn{11}{p{0.95\linewidth}}{\footnotesize Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\ref{tab:industry} and~\ref{tab:country_concentration}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries. Categories whose post share exceeds their baseline share: China-affiliated, 7 of the 9 AI/ML categories and 28 of the 31 others; company, 1 of 9 and 13 of 31; international, 2 of 9 and 11 of 31.}\\	53	+\multicolumn{9}{p{0.95\linewidth}}{\footnotesize Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\ref{tab:industry} and~\ref{tab:country_concentration}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries; works per category and period are in Part~IX. Categories whose post share exceeds their baseline share: China-affiliated, 7 of the 9 AI/ML categories and 28 of the 31 others; company, 1 of 9 and 13 of 31; international, 2 of 9 and 11 of 31.}\\
54	\end{longtable}	54	\end{longtable}

modified manuscript/tables/tab_net_category_gini.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_impact_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/category_impact_yearly.csv
2	\begin{longtable}{lrrrrrrrrr}	2	\begin{longtable}{lrrrrrrrrr}
3	\caption{Gini coefficient of 36-month citations $c^{(36)}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.}\label{tab:net_category_gini}\\	3	\caption{Gini coefficient of 36-month citations $c^{(36)}$ by primary category and publication year, for years with a complete window; baseline mean, change of the last complete year against it, and share uncited at 36 months in that year.}\label{tab:net_category_gini}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_net_category_to_ai.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_flows_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_flows_yearly.csv
2	\begin{longtable}{lrrrrrrrrrr}	2	\begin{longtable}{lrrrrrrrrrr}
3	\caption{Share of each category's internal references received by the nine categories of the AI/ML area, by citing year (per cent).}\label{tab:net_category_to_ai}\\	3	\caption{Share of each category's internal references received by the nine categories of the AI/ML area, by citing year (per cent).}\label{tab:net_category_to_ai}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_net_gini_ai_vs_rest.tex +11 −11

				`@@ -1,17 +1,17 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/category_impact_yearly.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/category_impact_yearly.csv
2	−\begin{table}[htbp]\centering\caption{Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.}\label{tab:net_gini_ai_vs_rest}	2	+\begin{table}[htbp]\centering\caption{Citation inequality at 36 months, AI/ML area against all other areas, by publication year with a complete window.}\label{tab:net_gini_ai_vs_rest}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	−\begin{tabular}{lrrrrrrrrrr}	4	+\begin{tabular}{lrrrrrr}
5	\toprule	5	\toprule
6	−\textbf{Year} & \textbf{Works, AI/ML} & \textbf{Works, other} & \textbf{Gini, AI/ML} & \textbf{Gini, other} & \textbf{Mean c36, AI/ML} & \textbf{Mean c36, other} & \textbf{Uncited, AI/ML (\%)} & \textbf{Uncited, other (\%)} & \textbf{Top decile FWCI, AI/ML (\%)} & \textbf{Top decile FWCI, other (\%)} \\	6	+\textbf{Year} & \textbf{Gini, AI/ML} & \textbf{Gini, other} & \textbf{Uncited, AI/ML (\%)} & \textbf{Uncited, other (\%)} & \textbf{Top decile FWCI, AI/ML (\%)} & \textbf{Top decile FWCI, other (\%)} \\
7	\midrule	7	\midrule
8	−2018 & 38{,}148 & 460{,}583 & 0.799 & 0.796 & 7.26 & 4.99 & 37.7 & 43.4 & 15.8 & 15.0 \\	8	+2018 & 0.799 & 0.796 & 37.7 & 43.4 & 15.8 & 15.0 \\
9	−2019 & 47{,}860 & 491{,}076 & 0.779 & 0.787 & 7.81 & 5.68 & 34.5 & 40.6 & 15.6 & 15.0 \\	9	+2019 & 0.779 & 0.787 & 34.5 & 40.6 & 15.6 & 15.0 \\
10	−2020 & 54{,}806 & 492{,}618 & 0.773 & 0.775 & 8.21 & 6.38 & 32.0 & 37.3 & 15.3 & 14.6 \\	10	+2020 & 0.773 & 0.775 & 32.0 & 37.3 & 15.3 & 14.6 \\
11	−2021 & 60{,}987 & 495{,}830 & 0.765 & 0.760 & 7.64 & 6.56 & 31.5 & 33.8 & 15.3 & 14.9 \\	11	+2021 & 0.765 & 0.760 & 31.5 & 33.8 & 15.3 & 14.9 \\
12	−2022 & 63{,}609 & 493{,}388 & 0.755 & 0.749 & 7.83 & 6.71 & 29.4 & 31.4 & 15.9 & 15.3 \\	12	+2022 & 0.755 & 0.749 & 29.4 & 31.4 & 15.9 & 15.3 \\
13	−2023 & 82{,}170 & 548{,}963 & 0.787 & 0.765 & 8.98 & 6.64 & 32.4 & 34.0 & 15.8 & 14.8 \\	13	+2023 & 0.787 & 0.765 & 32.4 & 34.0 & 15.8 & 14.8 \\
14	\bottomrule	14	\bottomrule
15	\end{tabular}	15	\end{tabular}
16	−\begin{tablenotes}\footnotesize\item Group statistics are work-weighted means of the category statistics of Table~\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top decile FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.\end{tablenotes}	16	+\begin{tablenotes}\footnotesize\item Group statistics are work-weighted means of the category statistics of Table~\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group); group sizes are the sums of the category counts of \texttt{category\_impact\_yearly.csv}. Top decile FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.\end{tablenotes}
17	\end{threeparttable}\end{table}	17	\end{threeparttable}\end{table}

modified manuscript/tables/tab_net_modularity.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/modularity_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/cross_area_share_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/modularity_yearly.csv, ./results/graph/cross_area_share_yearly.csv
2	\begin{table}[htbp]\centering\caption{Modularity of the yearly category citation-flow network at the category and area partitions, the within-category self-share, and the aggregate cross-area share of internal references, raw and composition-adjusted.}\label{tab:net_modularity}	2	\begin{table}[htbp]\centering\caption{Modularity of the yearly category citation-flow network at the category and area partitions, the within-category self-share, and the aggregate cross-area share of internal references, raw and composition-adjusted.}\label{tab:net_modularity}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrr}	4	\begin{tabular}{lrrrrrr}

modified manuscript/tables/tab_reference_coverage.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/reference_coverage_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/reference_coverage_yearly.csv
2	\begin{table}[htbp]\centering\caption{Reference coverage by citing year: declared, resolved, internal and external references of eligible works.}\label{tab:reference_coverage}	2	\begin{table}[htbp]\centering\caption{Reference coverage by citing year: declared, resolved, internal and external references of eligible works.}\label{tab:reference_coverage}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrr}	4	\begin{tabular}{lrrrrrrr}

modified manuscript/tables/tab_security_counts.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv
2	\begin{longtable}{lrrrrrrrrrr}	2	\begin{longtable}{lrrrrrrrrrr}
3	\caption{Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).}\label{tab:security_counts}\\	3	\caption{Eligible works in the five security-and-privacy categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).}\label{tab:security_counts}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_security_landmarks.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/security_privacy_landmarks.csv	1	+% generated by tables/make_tables.py from ./literature_synthesis/landmarks/security_privacy_landmarks.csv
2	\begin{table}[htbp]\centering\caption{Composition of the automatically selected landmark set of the security-and-privacy area, by category.}\label{tab:security_landmarks}	2	\begin{table}[htbp]\centering\caption{Composition of the automatically selected landmark set of the security-and-privacy area, by category.}\label{tab:security_landmarks}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_security_llm_cooccurrence.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_cooccurrence_yearly.csv, ./results/graph/category_counts_yearly.csv
2	\begin{longtable}{lrrrrrrrrrrrrrr}	2	\begin{longtable}{lrrrrrrrrrrrrrr}
3	\caption{Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.}\label{tab:security_llm_cooccurrence}\\	3	\caption{Co-classification of security-and-privacy works with the LLM, AI-safety and AI-agents categories, by publication year: number of works carrying both categories and share of the security category's works.}\label{tab:security_llm_cooccurrence}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_security_summary.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv, ./results/econometrics/breaks.csv
2	\begin{table}[htbp]\centering\caption{Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).}\label{tab:security_summary}	2	\begin{table}[htbp]\centering\caption{Growth and structural-change summary for the five security-and-privacy categories (multi-label counts).}\label{tab:security_summary}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrrrr}

modified manuscript/tables/tab_source_types.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/top_sources_by_period.csv
2	\begin{table}[htbp]\centering\caption{Distribution of works over source types among the listed sources, by period (per cent).}\label{tab:source_types}	2	\begin{table}[htbp]\centering\caption{Distribution of works over source types among the listed sources, by period (per cent).}\label{tab:source_types}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrr}	4	\begin{tabular}{lrrrrrrr}

modified manuscript/tables/tab_systems_categories.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/cagr_by_category.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/cagr_by_category.csv, ./results/econometrics/breaks.csv
2	\begin{table}[htbp]\centering\caption{The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.}\label{tab:systems_categories}	2	\begin{table}[htbp]\centering\caption{The systems and infrastructure categories of Part VI: primary-category output, compound annual growth, and structural-change estimates of the monthly log-volume series.}\label{tab:systems_categories}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_theory_counts.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv
2	\begin{longtable}{lrrrrrrrrrrrr}	2	\begin{longtable}{lrrrrrrrrrrrr}
3	\caption{Eligible works in the six theory and software categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).}\label{tab:theory_counts}\\	3	\caption{Eligible works in the six theory and software categories by publication year: works carrying the category (all) and works whose primary category it is (prim.).}\label{tab:theory_counts}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_theory_landmark_audit.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_G/contributions.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/theory_landmarks.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/landmarks/software_pl_landmarks.csv	1	+% generated by tables/make_tables.py from ./literature_synthesis/agent_G/contributions.csv, ./literature_synthesis/landmarks/theory_landmarks.csv, ./literature_synthesis/landmarks/software_pl_landmarks.csv
2	\begin{table}[htbp]\centering\caption{Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.}\label{tab:theory_landmark_audit}	2	\begin{table}[htbp]\centering\caption{Specialist audit of the automatically selected landmark candidates of the theory and software areas, by verdict and primary category.}\label{tab:theory_landmark_audit}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrr}

modified manuscript/tables/tab_theory_landmarks_discussed.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/literature_synthesis/agent_G/contributions.csv	1	+% generated by tables/make_tables.py from ./literature_synthesis/agent_G/contributions.csv
2	\begin{longtable}{lrrrrr}	2	\begin{longtable}{lrrrrr}
3	\caption{Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.}\label{tab:theory_landmarks_discussed}\\	3	\caption{Landmark candidates of the theory and software areas retained as in-scope research or survey contributions, with the contribution type judged from the abstract or metadata.}\label{tab:theory_landmarks_discussed}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_theory_llm_cooccurrence.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_cooccurrence_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_cooccurrence_yearly.csv, ./results/graph/category_counts_yearly.csv
2	\begin{longtable}{lrrrrrrrrrrrr}	2	\begin{longtable}{lrrrrrrrrrrrr}
3	\caption{Co-classification of theory and software works with the LLM category, by publication year: number of works carrying both categories and share of the category's multi-label count.}\label{tab:theory_llm_cooccurrence}\\	3	\caption{Co-classification of theory and software works with the LLM category, by publication year: number of works carrying both categories and share of the category's multi-label count.}\label{tab:theory_llm_cooccurrence}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_theory_summary.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/graph/category_counts_yearly.csv, /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/econometrics/breaks.csv	1	+% generated by tables/make_tables.py from ./results/graph/category_counts_yearly.csv, ./results/econometrics/breaks.csv
2	\begin{table}[htbp]\centering\caption{Growth and structural-change summary for the six theory and software categories (multi-label counts).}\label{tab:theory_summary}	2	\begin{table}[htbp]\centering\caption{Growth and structural-change summary for the six theory and software categories (multi-label counts).}\label{tab:theory_summary}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrrrrrrrr}	4	\begin{tabular}{lrrrrrrrrrrr}

modified manuscript/tables/tab_top_cited.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_cited_by_year.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/top_cited_by_year.csv
2	\begin{longtable}{lrrrr}	2	\begin{longtable}{lrrrr}
3	\caption{The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.}\label{tab:top_cited}\\	3	\caption{The ten most cited works of each publication year by 36-month window citations (c36), with total citations to date.}\label{tab:top_cited}\\
4	\toprule	4	\toprule

modified manuscript/tables/tab_top_institutions.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_institutions_by_period.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/top_institutions_by_period.csv
2	\begin{table}[htbp]\centering\caption{The twenty-five institutions with the largest fractional output in the post period, with their baseline output.}\label{tab:top_institutions}	2	\begin{table}[htbp]\centering\caption{The twenty-five institutions with the largest fractional output in the post period, with their baseline output.}\label{tab:top_institutions}
3	\begin{threeparttable}\small	3	\begin{threeparttable}\small
4	\begin{tabular}{lrrrrr}	4	\begin{tabular}{lrrrrr}

modified manuscript/tables/tab_top_sources.tex +1 −1

				`@@ -1,4 +1,4 @@`
1	−% generated by tables/make_tables.py from /Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/informatique/wp44/results/bibliometrics/top_sources_by_period.csv	1	+% generated by tables/make_tables.py from ./results/bibliometrics/top_sources_by_period.csv
2	\begin{longtable}{lrrrrrr}	2	\begin{longtable}{lrrrrrr}
3	\caption{The twenty largest sources (venues) by period, with OpenAlex source type.}\label{tab:top_sources}\\	3	\caption{The twenty largest sources (venues) by period, with OpenAlex source type.}\label{tab:top_sources}\\
4	\toprule	4	\toprule

modified tables/make_tables.py +5 −5

				`@@ -410,11 +410,11 @@ def tables_part08_categories():`
410	for c in cats:	410	for c in cats:
411	if (c, "baseline") not in g.index or (c, "post") not in g.index: continue	411	if (c, "baseline") not in g.index or (c, "post") not in g.index: continue
412	b = g.loc[(c, "baseline")]; q = g.loc[(c, "post")]	412	b = g.loc[(c, "baseline")]; q = g.loc[(c, "post")]
413	− rows.append({"Category": cname[c], "Works, base": int(b.n), "Works, post": int(q.n), "CN, base": b.cn, "CN, post": q.cn, "US, base": b.us, "US, post": q.us, "Comp., base": b.company, "Comp., post": q.company, "Intl., base": b.intl, "Intl., post": q.intl})	413	+ rows.append({"Category": cname[c], "CN, base": b.cn, "CN, post": q.cn, "US, base": b.us, "US, post": q.us, "Comp., base": b.company, "Comp., post": q.company, "Intl., base": b.intl, "Intl., post": q.intl})
414	ra = pd.DataFrame(rows); ra["ai"] = [carea[c] == "ai_ml" for c in cats if (c, "baseline") in g.index and (c, "post") in g.index]	414	ra = pd.DataFrame(rows); ra["ai"] = [carea[c] == "ai_ml" for c in cats if (c, "baseline") in g.index and (c, "post") in g.index]
415	cnt = lambda col_b, col_p, ai: int(((ra[col_p] > ra[col_b]) & (ra.ai == ai)).sum()); nai = int(ra.ai.sum()); not_ = int((~ra.ai).sum())	415	cnt = lambda col_b, col_p, ai: int(((ra[col_p] > ra[col_b]) & (ra.ai == ai)).sum()); nai = int(ra.ai.sum()); not_ = int((~ra.ai).sum())
416	write("tab_net_category_actors", pd.DataFrame(rows), "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).", "tab:net_category_actors", [p], fmt={k: "{:.1f}" for k in ["CN, base", "CN, post", "US, base", "US, post", "Comp., base", "Comp., post", "Intl., base", "Intl., post"]}, longtable=True,	416	write("tab_net_category_actors", pd.DataFrame(rows), "Shares of works with a China-affiliated author, a United States-affiliated author, a company affiliation and authors in at least two countries, by primary category and period (per cent).", "tab:net_category_actors", [p], fmt={k: "{:.1f}" for k in ["CN, base", "CN, post", "US, base", "US, post", "Comp., base", "Comp., post", "Intl., base", "Intl., post"]}, longtable=True,
417	− note=f"Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\\ref{{tab:industry}} and~\\ref{{tab:country_concentration}}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries. Categories whose post share exceeds their baseline share: China-affiliated, {cnt('CN, base', 'CN, post', True)} of the {nai} AI/ML categories and {cnt('CN, base', 'CN, post', False)} of the {not_} others; company, {cnt('Comp., base', 'Comp., post', True)} of {nai} and {cnt('Comp., base', 'Comp., post', False)} of {not_}; international, {cnt('Intl., base', 'Intl., post', True)} of {nai} and {cnt('Intl., base', 'Intl., post', False)} of {not_}.")	417	+ note=f"Baseline: works dated 2018-01-01 to 2022-11-29; post: 2022-11-30 to 2026-11-02 (incomplete). Shares are over all works of the category, including works without affiliation data, so they are depressed where affiliation coverage is low (recent cohorts, preprint-heavy categories) and are not comparable with the typed-denominator shares of Tables~\\ref{{tab:industry}} and~\\ref{{tab:country_concentration}}; comparisons across periods within a category carry this caveat. Comp. = at least one company-typed affiliation; Intl. = at least two author countries; works per category and period are in Part~IX. Categories whose post share exceeds their baseline share: China-affiliated, {cnt('CN, base', 'CN, post', True)} of the {nai} AI/ML categories and {cnt('CN, base', 'CN, post', False)} of the {not_} others; company, {cnt('Comp., base', 'Comp., post', True)} of {nai} and {cnt('Comp., base', 'Comp., post', False)} of {not_}; international, {cnt('Intl., base', 'Intl., post', True)} of {nai} and {cnt('Intl., base', 'Intl., post', False)} of {not_}.")
418	a["grp"] = np.where(a.primary_category.map(carea) == "ai_ml", "AI/ML area", "Other areas"); h = a.groupby(["grp", "period"])[["n", "cn", "us", "company", "intl", "arxiv", "oa"]].sum()	418	a["grp"] = np.where(a.primary_category.map(carea) == "ai_ml", "AI/ML area", "Other areas"); h = a.groupby(["grp", "period"])[["n", "cn", "us", "company", "intl", "arxiv", "oa"]].sum()
419	for c in ["cn", "us", "company", "intl", "arxiv", "oa"]: h[c] = 100 * h[c] / h.n	419	for c in ["cn", "us", "company", "intl", "arxiv", "oa"]: h[c] = 100 * h[c] / h.n
420	rows = [{"Group": grp, "Period": pr, "Works": int(h.loc[(grp, pr)].n), "CN (%)": h.loc[(grp, pr)].cn, "US (%)": h.loc[(grp, pr)].us, "Company (%)": h.loc[(grp, pr)].company, "Intl. (%)": h.loc[(grp, pr)].intl, "arXiv (%)": h.loc[(grp, pr)].arxiv, "OA (%)": h.loc[(grp, pr)].oa} for grp in ["AI/ML area", "Other areas"] for pr in ["baseline", "post"]]	420	rows = [{"Group": grp, "Period": pr, "Works": int(h.loc[(grp, pr)].n), "CN (%)": h.loc[(grp, pr)].cn, "US (%)": h.loc[(grp, pr)].us, "Company (%)": h.loc[(grp, pr)].company, "Intl. (%)": h.loc[(grp, pr)].intl, "arXiv (%)": h.loc[(grp, pr)].arxiv, "OA (%)": h.loc[(grp, pr)].oa} for grp in ["AI/ML area", "Other areas"] for pr in ["baseline", "post"]]
				`@@ -442,9 +442,9 @@ def tables_part08_categories():`
442	note=f"Gini of $c^{{(36)}}$ over the works whose primary category is the row category (Section~\\ref{{sec:m-citations}}); a blank cell is a category with no works in the year. Base mean: mean over {base[0]}--{base[-1]}. The {last} window ends in {last + 2}, whose slower indexing channels were incomplete at the snapshot, so the {last} values carry the right-truncation caveat of Table~\\ref{{tab:citation_windows}}. Very small categories and categories whose {last} value is far from every earlier value may reflect single misattributed records (Section~\\ref{{sec:net-topcited}}). Count of categories whose {last} Gini exceeds their baseline mean: {up['ai_ml']} of the {tot_ai} AI/ML categories and {up['other']} of the {tot_ot} other categories.")	442	note=f"Gini of $c^{{(36)}}$ over the works whose primary category is the row category (Section~\\ref{{sec:m-citations}}); a blank cell is a category with no works in the year. Base mean: mean over {base[0]}--{base[-1]}. The {last} window ends in {last + 2}, whose slower indexing channels were incomplete at the snapshot, so the {last} values carry the right-truncation caveat of Table~\\ref{{tab:citation_windows}}. Very small categories and categories whose {last} value is far from every earlier value may reflect single misattributed records (Section~\\ref{{sec:net-topcited}}). Count of categories whose {last} Gini exceeds their baseline mean: {up['ai_ml']} of the {tot_ai} AI/ML categories and {up['other']} of the {tot_ot} other categories.")
443	i["grp"] = np.where(i.primary_category.map(carea) == "ai_ml", "AI/ML", "Other"); i["g"] = i.gini_c36 * i.n; i["u"] = i.share_uncited_36 * i.n; i["t10"] = i.share_top10_fwci * i.n; i["m"] = i.mean_c36 * i.n	443	i["grp"] = np.where(i.primary_category.map(carea) == "ai_ml", "AI/ML", "Other"); i["g"] = i.gini_c36 * i.n; i["u"] = i.share_uncited_36 * i.n; i["t10"] = i.share_top10_fwci * i.n; i["m"] = i.mean_c36 * i.n
444	h = i.groupby(["grp", "year"])[["n", "g", "u", "t10", "m"]].sum()	444	h = i.groupby(["grp", "year"])[["n", "g", "u", "t10", "m"]].sum()
445	− rows = [{"Year": str(y), "Works, AI/ML": int(h.loc[("AI/ML", y)].n), "Works, other": int(h.loc[("Other", y)].n), "Gini, AI/ML": h.loc[("AI/ML", y)].g / h.loc[("AI/ML", y)].n, "Gini, other": h.loc[("Other", y)].g / h.loc[("Other", y)].n, "Mean c36, AI/ML": h.loc[("AI/ML", y)].m / h.loc[("AI/ML", y)].n, "Mean c36, other": h.loc[("Other", y)].m / h.loc[("Other", y)].n, "Uncited, AI/ML (%)": 100 * h.loc[("AI/ML", y)].u / h.loc[("AI/ML", y)].n, "Uncited, other (%)": 100 * h.loc[("Other", y)].u / h.loc[("Other", y)].n, "Top decile FWCI, AI/ML (%)": 100 * h.loc[("AI/ML", y)].t10 / h.loc[("AI/ML", y)].n, "Top decile FWCI, other (%)": 100 * h.loc[("Other", y)].t10 / h.loc[("Other", y)].n} for y in yrs]	445	+ rows = [{"Year": str(y), "Gini, AI/ML": h.loc[("AI/ML", y)].g / h.loc[("AI/ML", y)].n, "Gini, other": h.loc[("Other", y)].g / h.loc[("Other", y)].n, "Uncited, AI/ML (%)": 100 * h.loc[("AI/ML", y)].u / h.loc[("AI/ML", y)].n, "Uncited, other (%)": 100 * h.loc[("Other", y)].u / h.loc[("Other", y)].n, "Top decile FWCI, AI/ML (%)": 100 * h.loc[("AI/ML", y)].t10 / h.loc[("AI/ML", y)].n, "Top decile FWCI, other (%)": 100 * h.loc[("Other", y)].t10 / h.loc[("Other", y)].n} for y in yrs]
446	− write("tab_net_gini_ai_vs_rest", pd.DataFrame(rows), "Citation inequality and levels at 36 months, AI/ML area against all other areas, by publication year with a complete window.", "tab:net_gini_ai_vs_rest", [p], fmt={"Gini, AI/ML": "{:.3f}", "Gini, other": "{:.3f}", "Mean c36, AI/ML": "{:.2f}", "Mean c36, other": "{:.2f}", "Uncited, AI/ML (%)": "{:.1f}", "Uncited, other (%)": "{:.1f}", "Top decile FWCI, AI/ML (%)": "{:.1f}", "Top decile FWCI, other (%)": "{:.1f}"},	446	+ write("tab_net_gini_ai_vs_rest", pd.DataFrame(rows), "Citation inequality at 36 months, AI/ML area against all other areas, by publication year with a complete window.", "tab:net_gini_ai_vs_rest", [p], fmt={"Gini, AI/ML": "{:.3f}", "Gini, other": "{:.3f}", "Uncited, AI/ML (%)": "{:.1f}", "Uncited, other (%)": "{:.1f}", "Top decile FWCI, AI/ML (%)": "{:.1f}", "Top decile FWCI, other (%)": "{:.1f}"},
447	− note="Group statistics are work-weighted means of the category statistics of Table~\\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group). Top decile FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.")	447	+ note="Group statistics are work-weighted means of the category statistics of Table~\\ref{tab:net_category_gini} (the Gini is therefore a weighted mean of within-category Ginis, not the Gini of the pooled group); group sizes are the sums of the category counts of \\texttt{category\\_impact\\_yearly.csv}. Top decile FWCI: share of works in the top decile of OpenAlex field-weighted citation impact. The last year carries the right-truncation caveat.")
448		448
449		449
450		450
451		451

spb/wp44_uqo

WP44: Part VIII category-level knowledge flows, actors and inequality (8 tables)

Theme