Findings
Measurements too small for a paper and too specific for an essay. Each one states what it found, on what data, and what it does not support. The caveats live here with the finding rather than in a reply somewhere, because a chart travels and its footnotes do not.
Register drift in AI-signed code review
Where AI-signed prose sits on the grammatical axis between US-located and India-located professional English, and which way each model is moving. Drawn from 153,448 GitHub comments, 2022 to 2026, parsed for construction rather than vocabulary and matched on repository and year.
Reading and caveats
The four Figure 1 metrics are the constructions that most separate India-located from US-located professional GitHub prose in 2022–24 (all p < 10−30, repo- and year-matched samples of ~4,800 comments per side): verbless status sentences, low clause subordination, triple-noun compounds and please/kindly politeness marking.
Claude-signed text enters 2025 past the India baseline on all four at once and drifts back toward the human range through 2026. Copilot shares the noun compression but keeps US-style subordination and has converged to US politeness. A third cohort of 370 AI-signed comments whose signature names no model (chiefly Claude Code PR footers with the link text stripped at harvest) is excluded from the charts: its register does not track the Claude cohort, and whether that reflects different authorship or human editing of generated text cannot be determined from the corpus.
Limits: the 2025 cells rest on 47–57 texts each and the Copilot 2026 cells on ~30, so
read levels loosely and trends cautiously. AI-signed text is largely PR-body prose while
the baselines are conversation comments; that genre gap inflates verblessness and noun
stacking for every AI cohort, though it cannot manufacture the politeness or
subordination signatures. Parsing is spaCy en_core_web_sm; the parser is
trained on US-style English, which may flatten Indian-text parses slightly.