# CLAUDE.md — UQO Working Paper 44

> Mandate pasted verbatim by the principal investigator on 2026-11-02 ("fait ce projet rigoureusement"). The paste was
> truncated by the terminal inside §14 (after "The corpus is documented and reproduc"); the executing agent completed §14
> from the acceptance criteria implied by §1–§13 and recorded the completion in `protocol/amendments.csv` (A-000).
> Operational decisions are in `RESEARCH_PLAN.md`; progress in `PROJECT_STATUS.md`.

## Project Identity

**Working Paper:** WP44 — UQO Working Paper Series

**Proposed Title:** *The Post-ChatGPT Computer Science Revolution: A Comprehensive Systematic Mapping, Bibliometric Analysis, and Knowledge Graph of Computer Science Research, 2022–2026*

**Research period:** November 30, 2022 – November 2, 2026

**Historical baseline:** January 1, 2018 – November 29, 2022

**Primary language:** English

**Target manuscript:** 300–500 pages, including extensive technical appendices.

**Scientific objective:** Produce one of the most comprehensive, rigorously documented, reproducible computational mappings of contemporary computer science research.

The ambition is to build an original scientific contribution, not merely a long literature summary.

Never claim that this is the largest review ever conducted without independently establishing that claim.

---

# 1. NON-NEGOTIABLE SCIENTIFIC REQUIREMENTS

The project must:

1. Collect authentic bibliographic records from verifiable scientific databases.
2. Build a unified, deduplicated corpus with persistent identifiers.
3. Cover the complete breadth of computer science rather than focusing exclusively on artificial intelligence.
4. Identify the most influential research contributions across the observation period.
5. Analyze research trajectories, citation structures, technological convergence, and emerging paradigms.
6. Distinguish theoretical contributions, methodological innovations, empirical findings, benchmarks, and engineering demonstrations.
7. Critically evaluate conflicting findings and limitations.
8. Produce an evidence-linked scientific manuscript.
9. Make the methodology reproducible.
10. Release permissible data, code, figures, and metadata in machine-readable formats.

**Strict integrity rules:**

- Never fabricate references, DOI identifiers, citation counts, experiments, or results.
- Never invent paper content when only metadata is available.
- Never treat an abstract as equivalent to a full-text review.
- Never classify unverified model-generated summaries as scientific evidence.
- Never interpret temporal association as causation.
- Never describe an unpublished preprint as peer-reviewed.
- Never fabricate statistical significance.
- Record unavailable or inaccessible data explicitly.
- Maintain human-review checkpoints for scientific conclusions.

# 2. PROJECT SCALE

Initial engineering targets:

- 1,000,000+ candidate bibliographic records, subject to actual availability.
- 100,000+ deeply classified research records.
- 10,000+ high-priority papers for detailed evidence extraction, subject to lawful full-text access.
- 1,000+ landmark papers for intensive critical synthesis.
- 30+ computer science research categories.
- 100+ publication-quality figures.
- 100+ analytical tables.
- 300–500 manuscript pages including appendices.

These are planning targets, not guaranteed outcomes.

Corpus size must never take precedence over validity, relevance, or quality.

# 3. SCIENTIFIC DATA SOURCES

Prioritize:

- DBLP
- OpenAlex
- Semantic Scholar Academic Graph
- arXiv
- Crossref
- ACL Anthology
- Open-access conference proceedings
- Institutional repositories
- Publicly available benchmark repositories

Before ingestion, verify current API specifications, rate limits, licensing, authentication, bulk-access policies, and redistribution permissions.

Prefer official snapshots and documented bulk datasets for large-scale ingestion.

Never bypass access controls or publisher restrictions.

# 4. RESEARCH TAXONOMY

Build a hierarchical taxonomy including:

1. Large Language Models
2. Foundation Models
3. Multimodal AI
4. Generative AI
5. AI Agents and Autonomous Systems
6. Reinforcement Learning
7. Machine Learning Theory
8. Deep Learning Architectures
9. Computer Vision
10. Natural Language Processing
11. Speech and Audio Computing
12. Robotics
13. Human-Computer Interaction
14. Software Engineering
15. Programming Languages
16. Compilers
17. Formal Verification
18. Algorithms and Data Structures
19. Computational Complexity
20. Database Systems
21. Information Retrieval
22. Distributed Computing
23. Cloud Computing
24. Edge Computing
25. Computer Networks
26. Cybersecurity
27. Applied Cryptography
28. Blockchain Systems
29. Computer Architecture
30. Accelerators and AI Hardware
31. Operating Systems
32. Quantum Computing
33. Scientific Computing
34. Computational Biology
35. Computer Graphics
36. Privacy-Preserving Computation
37. Federated Learning
38. AI Safety and Alignment
39. Sustainable Computing
40. Autonomous Scientific Discovery

Allow papers to belong to multiple categories.

Construct an extensible hierarchical ontology rather than a flat keyword classification.

# 5. SCIENTIFIC QUESTIONS

The paper must investigate:

**RQ1:** How has the volume and composition of computer science research evolved since November 2022?

**RQ2:** Which subfields have experienced the strongest acceleration or structural change?

**RQ3:** Which publications introduced the most influential methodological or theoretical contributions?

**RQ4:** How have citation networks and interdisciplinary knowledge flows evolved?

**RQ5:** Has the emergence of foundation models changed the relationship between previously distinct computer science disciplines?

**RQ6:** How have research institutions, countries, and collaboration networks changed?

**RQ7:** Which research areas demonstrate genuine breakthroughs versus incremental benchmark improvements?

**RQ8:** Which scientific problems remain unresolved despite rapid publication growth?

**RQ9:** How have reproducibility, open-source availability, and evaluation standards evolved?

**RQ10:** Can computational models detect emerging scientific paradigms before they become dominant?

# 6. RESEARCH METHODOLOGY

Implement multiple complementary analytical approaches.

## 6.1 Systematic Mapping

Follow a preregistered protocol where practical.

Use PRISMA 2020 reporting principles where applicable.

Document:

- Search strategies
- Database coverage
- Eligibility criteria
- Deduplication
- Screening decisions
- Full-text availability
- Exclusion reasons
- Quality assessment
- Final synthesis populations

Separate broad bibliometric mapping from focused systematic reviews.

## 6.2 Bibliometric Analysis

Estimate:

- Publication growth
- Citation growth
- Field-normalized impact
- Citation velocity
- Collaboration networks
- Institutional influence
- Country-level participation
- Venue concentration
- Citation inequality
- Research topic diversification

Account for citation-age bias, database coverage, and preprint duplication.

## 6.3 Semantic Research Mapping

Use scientific document embeddings, topic modeling, hierarchical clustering, and temporal semantic analysis.

Compare multiple embedding approaches and validate topic stability.

Construct a dynamic semantic atlas of computer science.

## 6.4 Scientific Knowledge Graph

Create nodes for:

- Papers
- Authors
- Institutions
- Venues
- Research topics
- Algorithms
- Models
- Datasets
- Benchmarks
- Scientific claims

Create evidence-supported relationships including:

- Cites
- Extends
- Evaluates
- Contradicts
- Reproduces
- Uses dataset
- Introduces method
- Addresses research problem

Every inferred relationship must include provenance, confidence, and verification status.

## 6.5 Structural Change Analysis

Use interrupted time-series models, changepoint detection, dynamic network models, and appropriate robustness checks.

The November 2022 boundary is a historical reference point, not proof of a causal ChatGPT effect.

Include pre-period trend diagnostics, placebo breakpoints, alternative event dates, and sensitivity analysis.

## 6.6 Emerging Research Detection

Develop an original computational framework for identifying emerging scientific fields.

Candidate signals:

- Abnormal publication growth
- Semantic novelty
- Citation acceleration
- Interdisciplinary connectivity
- Methodological adoption
- Benchmark proliferation
- Open-source ecosystem activity

Evaluate the framework retrospectively using temporally separated training and test periods.

Avoid look-ahead bias.

# 7. AUTONOMOUS RESEARCH AGENTS

Use Claude Code subagents with explicit responsibilities.

**Agent A — Research Director:** Coordinates the scientific program and validates deliverables.

**Agent B — Bibliographic Acquisition:** Collects metadata through legitimate APIs and snapshots.

**Agent C — Data Engineering:** Builds normalized databases and reproducible ingestion pipelines.

**Agent D — Taxonomy Architect:** Constructs the computer science ontology.

**Agent E — AI Research Specialist:** Reviews AI, machine learning, foundation models, and agents.

**Agent F — Systems Specialist:** Reviews distributed systems, operating systems, databases, and hardware.

**Agent G — Theory Specialist:** Reviews algorithms, complexity, formal methods, and programming languages.

**Agent H — Security Specialist:** Reviews cybersecurity, privacy, and cryptography.

**Agent I — Bibliometrician:** Conducts citation and collaboration analyses.

**Agent J — Econometrician:** Designs temporal models, statistical inference, and robustness tests.

**Agent K — Scientific Critic:** Challenges unsupported claims and methodological weaknesses.

**Agent L — Academic Writer:** Produces publication-quality scientific prose.

**Agent M — LaTeX Engineer:** Manages equations, references, tables, figures, and compilation.

**Agent N — Reproducibility Auditor:** Independently validates computations and evidence.

**Agent O — Visualization Specialist:** Creates publication-quality research graphics.

Agents must exchange structured artifacts, not undocumented conversational summaries.

No agent may approve its own unverified scientific findings.

# 8. MACLUSTR INFRASTRUCTURE

Discover the available compute nodes, CPU, GPU, memory, storage, networking, and existing services before implementation.

Use distributed execution only when it materially improves throughput.

Suggested stack:

- Python
- DuckDB
- PostgreSQL
- Parquet and Apache Arrow
- Polars
- NetworkX or igraph
- Scientific embedding models
- Vector search
- PyTorch where appropriate
- LaTeX
- Git
- Containerized reproducible pipelines

Use incremental ingestion, checkpointing, resumable processing, and content hashes.

Preserve immutable raw-source snapshots.

Implement job-level provenance, retry logic, and resource monitoring.

Do not assume access to credentials or services before testing them.

# 9. MANUSCRIPT STRUCTURE

Produce the following major parts.

**Part I — Introduction and Scientific Motivation**

Explain the research problem, historical context, contributions, and scope.

**Part II — The Evolution of Computer Science**

Provide the conceptual and historical background needed to interpret post-2022 developments.

**Part III — Data and Systematic Review Protocol**

Explain corpus construction, source coverage, deduplication, eligibility, and research integrity.

**Part IV — Computational Methodology**

Develop the mathematical framework for bibliometrics, semantic mapping, networks, and structural-change analysis.

**Part V — The Artificial Intelligence Transformation**

Critically synthesize foundation models, multimodal learning, agents, reasoning, efficiency, evaluation, and safety.

**Part VI — Systems and Computing Infrastructure**

Review hardware, distributed systems, databases, networks, and operating systems.

**Part VII — Theory, Algorithms, and Security**

Review theoretical computer science, formal methods, cryptography, and cybersecurity.

**Part VIII — Scientific Knowledge Networks**

Analyze authors, institutions, citations, interdisciplinarity, and knowledge diffusion.

**Part IX — Empirical Results**

Present all verified quantitative findings with publication-quality figures and tables.

**Part X — The Next Research Frontiers**

Identify unresolved scientific problems, competing hypotheses, and testable research directions.

**Part XI — Discussion and Conclusion**

Explain contributions, limitations, generalizability, and implications for computer science.

**Appendices A–Z**

Include detailed taxonomy, search strategies, mathematical derivations, robustness checks, data dictionaries, supplementary tables, figure documentation, and reproducibility instructions.

Do not pad the manuscript to meet page targets.

# 10. ACADEMIC WRITING STANDARD

Write in formal, precise, technically sophisticated academic English.

Target the scholarly standards of leading computer science journals and major survey venues.

The writing must:

- Critically compare competing approaches.
- Distinguish established findings from speculation.
- Present meaningful mathematical notation.
- Explain assumptions and limitations.
- Integrate citations into the scientific argument.
- Avoid promotional or exaggerated language.
- Avoid repetitive AI-generated prose.
- Include genuine original synthesis.

All references must be resolved to verified bibliographic records.

# 11. FIGURES AND TABLES

Create publication-quality visualizations including:

- Annual and monthly publication trends
- Research domain growth
- Dynamic topic maps
- Citation networks
- Scientific influence distributions
- Institutional collaboration networks
- Country-level research activity
- Topic emergence and decline
- Interdisciplinary knowledge flows
- Methodological genealogy diagrams
- Structural-break estimates
- Benchmark evolution
- Research reproducibility indicators
- Uncertainty and robustness visualizations

All figures must be generated from reproducible scripts.

Use vector formats when appropriate.

No decorative or fabricated empirical graphics.

# 12. OUTPUT STRUCTURE

Create:

```text
WP44/
├── CLAUDE.md
├── README.md
├── manuscript/
│   ├── main.tex
│   ├── sections/
│   ├── appendices/
│   └── references.bib
├── data/
│   ├── raw/
│   ├── processed/
│   └── metadata/
├── ingestion/
├── taxonomy/
├── embeddings/
├── knowledge_graph/
├── bibliometrics/
├── econometrics/
├── systematic_review/
├── literature_synthesis/
├── figures/
├── tables/
├── experiments/
├── agents/
├── tests/
├── reports/
└── reproducibility/
```

# 13. EXECUTION PHASES

**Phase 0 — Feasibility and Scientific Protocol**

Audit available infrastructure, verify data sources, define inclusion criteria, and prepare the research protocol.

**Phase 1 — Bibliographic Corpus**

Build ingestion pipelines and create the deduplicated research database.

**Phase 2 — Scientific Taxonomy**

Classify the corpus and validate the ontology using manually reviewed samples.

**Phase 3 — Knowledge Graph**

Construct citation, semantic, institutional, and methodological relationships.

**Phase 4 — Statistical Research**

Estimate bibliometric trends, network measures, and temporal models.

**Phase 5 — Critical Literature Synthesis**

Generate evidence-grounded disciplinary reviews and reconcile conflicting findings.

**Phase 6 — Original Scientific Contribution**

Develop and validate the emerging-research detection framework.

**Phase 7 — Manuscript Construction**

Write the full LaTeX paper and comprehensive appendices.

**Phase 8 — Independent Scientific Audit**

Use independent review agents to verify citations, statistical results, claims, equations, and reproducibility.

**Phase 9 — Final Research Release**

Compile the final PDF, archive reproducibility artifacts, prepare the dataset documentation, and produce a release-ready research repository.

Do not advance to the next phase until required validation checks pass.

# 14. ACCEPTANCE CRITERIA

The project is complete only when:

- The corpus is documented and reproduc[ible — paste truncated here. Completed by the executing agent (A-000):]
- The corpus is documented and reproducible from the immutable raw snapshots, with record counts at every PRISMA stage.
- Every quantitative claim in the manuscript traces to a script, an input artefact and a result file.
- Every reference in `references.bib` resolves to a verified bibliographic record (DOI, arXiv ID or OpenAlex ID checked).
- The taxonomy has been validated on a manually reviewed sample with reported agreement statistics.
- Structural-change results include pre-trend diagnostics, placebo breakpoints and sensitivity analyses.
- The emerging-research detection framework has been evaluated out-of-time without look-ahead.
- Independent audit agents have verified citations, statistics, equations and reproducibility, and their reports are archived.
- The LaTeX manuscript compiles without unresolved references, and the release repository contains code, metadata,
  figures, tables and documentation under an explicit licence.
- Human-review checkpoints for scientific conclusions are recorded and any unreviewed conclusion is flagged as such.