1# Brief — Part II "The Evolution of Computer Science" (file manuscript/sections/part02_evolution.tex; bib manuscript/references_part02.bib)23Target 25–35 pages (≈ 12,000–16,000 words). Purpose (CLAUDE.md §9): the conceptual and historical background needed to4interpret post-2022 developments — not a history of computing in general. Suggested sections (adapt):51. The disciplinary structure of computer science (ACM CCS, the 40-category ontology of this paper, how fields relate; how6 bibliometric field classifications — OpenAlex topics, DBLP venues, arXiv categories — encode that structure and where they disagree).72. Three prior transformations and how they propagated across subfields: the internet/web era, the deep-learning wave8 (2012–2017: AlexNet, ResNet, seq2seq, attention), the transformer/pretraining era (2017–2022: Transformer, BERT, GPT-2/3,9 scaling laws, CLIP, diffusion, AlphaFold). For each: what changed in publication practice (preprints, benchmarks, industry labs).103. The state of each non-AI area at the baseline (2018–2022): systems (cloud, serverless, disaggregation), hardware11 (end of Dennard scaling, accelerators, "golden age"), theory (fine-grained complexity, learning theory), PL/verification12 (Rust, proof assistants), security/crypto (post-quantum standardisation, ML security), HCI, graphics (NeRF), quantum (NISQ),13 bioinformatics (AlphaFold 2) — concise, citation-backed.144. November 2022 as a reference date: what ChatGPT was and was not (an interface + RLHF over GPT-3.5), the preceding15 releases (InstructGPT, Codex, Stable Diffusion), why the date is used descriptively and not causally (CLAUDE.md §6.5).165. What the literature already claims about the post-2022 transformation (growth of AI papers, LLM-assisted writing, arXiv17 submission surges, benchmark saturation, industry concentration) and why those claims need the corpus-level test of this paper.18Landmark references: verify each (group G/H of the reference brief). Coordinate with `manuscript/references.bib` if present.19