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Four classification systems are relevant to a corpus of the kind assembled here, and they encode the structure of the discipline in different ways. |
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−The \emph{ACM Computing Classification System} (CCS) is the only one produced by a professional society as an explicit ontology. Its 2012 revision replaced the 1998 four-level scheme with a poly-hierarchy of roughly two thousand concepts organised under about a dozen top-level nodes, among them ``Theory of computation'', ``Computing methodologies'' (which houses artificial intelligence, machine learning, computer graphics and simulation side by side), ``Computer systems organization'', ``Software and its engineering'', ``Security and privacy'', ``Human-centered computing'' and ``Networks'' \citep{rous2012ccs}. The CCS is a statement of how the ACM understood the field in 2012. It is applied by authors at submission time, is used almost exclusively in ACM venues, and is therefore sparse in any corpus that spans IEEE, Springer, Elsevier, open-access publishers and preprint servers. Its value for this study is conceptual rather than operational: it provides an independent, expert-designed reference against which the forty categories of CLAUDE.md~\S4 can be positioned, and it documents a pre-transformer view in which large language models, foundation models, AI agents and AI safety do not exist as concepts at all. |
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+The \emph{ACM Computing Classification System} (CCS) is the only one produced by a professional society as an explicit ontology. Its 2012 revision replaced the 1998 four-level scheme with a poly-hierarchy of roughly two thousand concepts organised under about a dozen top-level nodes, among them ``Theory of computation'', ``Computing methodologies'' (which houses artificial intelligence, machine learning, computer graphics and simulation side by side), ``Computer systems organization'', ``Software and its engineering'', ``Security and privacy'', ``Human-centered computing'' and ``Networks'' \citep{rous2012ccs}. The CCS is a statement of how the ACM understood the field in 2012. It is applied by authors at submission time, is used almost exclusively in ACM venues, and is therefore sparse in any corpus that spans IEEE, Springer, Elsevier, open-access publishers and preprint servers. Its value for this study is conceptual rather than operational: it provides an independent, expert-designed reference against which the forty categories of CLAUDE.md, \S4 can be positioned, and it documents a pre-transformer view in which large language models, foundation models, AI agents and AI safety do not exist as concepts at all. |
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The \emph{arXiv subject classes} are the second system. arXiv was created in 1991 for high-energy physics and its taxonomy reflects that origin; the computer science archive (\texttt{cs}) is organised into forty subject classes largely inherited from the ACM scheme of the 1990s \citep{ginsparg2011arxiv}. Authors choose a primary class and may add cross-lists, so the classification is self-reported, multi-label, and coarse. Its distinctive property is temporal: a preprint is classified at the moment of submission, before peer review, so the arXiv class records how authors positioned their work at the time, not how a later indexer reconstructed it. For the questions of this paper that property matters: when a paper on, say, program verification with language models is submitted to \texttt{cs.PL} with a cross-list to \texttt{cs.LG}, the pair of classes is a small but genuine piece of evidence about disciplinary boundaries as practitioners perceive them. |
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\subsection{The forty-category ontology of this study} |
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\label{sec:p2-ontology} |
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−The classification used throughout the manuscript is the hierarchical ontology specified in CLAUDE.md~\S4 and built in Phase~2 (Appendix~A). It comprises forty leaf categories grouped into eight areas: artificial intelligence and machine learning (nine leaves, from large language models to AI safety); perception, language, robotics and interaction (six); software, programming languages and formal methods (four); theory of computation (two); data, information and distributed systems (six); security, privacy and trust (five); hardware, architecture and emerging computing (five); and computational science and discovery (three). A small number of \emph{specialisation} edges record that one leaf refines another: large language models and multimodal AI refine foundation models; compilers refine programming languages; cloud and edge computing refine distributed computing; federated learning refines privacy-preserving computation; AI accelerators refine computer architecture. The areas are a reporting device; classification operates on leaves. |
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+The classification used throughout the manuscript is the hierarchical ontology that the project mandate specifies (CLAUDE.md, \S4) and that Phase~2 built (Appendix~A). It comprises forty leaf categories grouped into eight areas: artificial intelligence and machine learning (nine leaves, from large language models to AI safety); perception, language, robotics and interaction (six); software, programming languages and formal methods (four); theory of computation (two); data, information and distributed systems (six); security, privacy and trust (five); hardware, architecture and emerging computing (five); and computational science and discovery (three). A small number of \emph{specialisation} edges record that one leaf refines another: large language models and multimodal AI refine foundation models; compilers refine programming languages; cloud and edge computing refine distributed computing; federated learning refines privacy-preserving computation; AI accelerators refine computer architecture. The areas are a reporting device; classification operates on leaves. |
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The assignment rule is deliberately simple so that it can be audited. Let $\mathcal{W}$ be the set of works and $\mathcal{C}$ the set of forty leaves. For each leaf $c$ the ontology lists weighted signals of five types $k \in \{\text{topic},\ \text{subfield},\ \text{arXiv},\ \text{stream},\ \text{keyword}\}$, with signal set $S_k(c)$ and weights $\omega_{kj} \in (0,1]$ for $j \in S_k(c)$. A work $w$ matches a signal $j$ if the corresponding attribute of $w$ (an OpenAlex topic identifier, a subfield, an arXiv class, a DBLP stream, or a regular expression over title and abstract) contains it. The score of $w$ for $c$ is |
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\begin{equation} |
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The questions of this paper are ultimately about rates: of publication, of citation, of movement between categories. Rates presuppose denominators, and the denominators of science are themselves contested quantities. |
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−The literature on the growth of science offers two sobering lessons. The first is that measured growth depends heavily on the database. Using cited references as a proxy for the literature that scientists actually use, \citet{bornmann2015growth} estimated the long-run growth of science at roughly three per cent per year with a doubling time of about a quarter of a century, while estimates based on the publication counts of individual databases vary widely with the database's coverage policy and its own expansion. A later analysis with segmented growth curves across several databases found that the growth rate has itself changed over time, with faster growth in recent decades, but that the location of the segments depends on the source \citep{bornmann2021growth}. For the present study the lesson is that a change in the volume of computer-science publications between 2018 and 2026 is the sum of a change in research activity and a change in indexing, and that only the first is of scientific interest. OpenAlex grew substantially during the period as it absorbed new sources and improved its matching; the indexing lag (the delay between a work's publication and its appearance in the snapshot) is longest for the most recent months, so that any series ending in 2026 slopes downward at its end for reasons that have nothing to do with research. These are among the pre-period diagnostics and sensitivity checks required by CLAUDE.md~\S6.5 and specified in Part~IV. |
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+The literature on the growth of science offers two sobering lessons. The first is that measured growth depends heavily on the database. Using cited references as a proxy for the literature that scientists actually use, \citet{bornmann2015growth} estimated the long-run growth of science at roughly three per cent per year with a doubling time of about a quarter of a century, while estimates based on the publication counts of individual databases vary widely with the database's coverage policy and its own expansion. A later analysis with segmented growth curves across several databases found that the growth rate has itself changed over time, with faster growth in recent decades, but that the location of the segments depends on the source \citep{bornmann2021growth}. For the present study the lesson is that a change in the volume of computer-science publications between 2018 and 2026 is the sum of a change in research activity and a change in indexing, and that only the first is of scientific interest. OpenAlex grew substantially during the period as it absorbed new sources and improved its matching; the indexing lag (the delay between a work's publication and its appearance in the snapshot) is longest for the most recent months, so that any series ending in 2026 slopes downward at its end for reasons that have nothing to do with research. These are among the pre-period diagnostics and sensitivity checks required by CLAUDE.md, \S6.5 and specified in Part~IV. |
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The second lesson concerns the ``science of science'' as a field in its own right. The programme described by \citet{fortunato2018sos} and developed at book length by \citet{wang2021sos} has established robust regularities: citation distributions are heavy-tailed within every field; team size has grown steadily; novelty in the sense of atypical combinations of prior work is associated with higher impact at the top of the distribution; and the productivity of individual scientists is approximately stable over careers. Those regularities are the background against which any field-specific claim must be read. A rise in the citation inequality of AI papers after 2022, for instance, is interesting only if it exceeds the inequality that heavy-tailed citation distributions produce in every growing field; the hypotheses H1--H8 of the preregistered protocol (Part~III) are formulated as comparisons between the post-2022 period and the baseline for that reason. |
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\subsection{Why the date is used descriptively and not causally} |
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\label{sec:p2-causal} |
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−The mandate of this study (CLAUDE.md~\S6.5) states that the November 2022 boundary is a historical reference point and not proof of a causal effect of ChatGPT. The statement is a constraint on interpretation, but it is also a methodological position that can be made precise. |
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+The mandate of this study (CLAUDE.md, \S6.5) states that the November 2022 boundary is a historical reference point and not proof of a causal effect of ChatGPT. The statement is a constraint on interpretation, but it is also a methodological position that can be made precise. |
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Let $N_{c,t}$ denote the number of works assigned to category $c$ in month $t$, and consider the simplest model that the ``post-ChatGPT'' framing suggests, an interrupted time series of the form used in programme evaluation \citep{bernal2016its}: |
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\begin{equation} |
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