Source: strategist-critic strategy review Critical issues: (1) Exposure index conflates displacement with augmentation — estimand is ambiguous. (2) Pre-trends begin 2019, three years before ChatGPT launch. Resolution required: Decompose exposure into substitution/complementarity components. Implement Rambachan & Roth (2023) sensitivity analysis. Re-run strategy review after fix.
Source: Average of domain-referee (73) and methods-referee (71) Key concerns: Both referees flag the exposure decomposition as essential. Methods referee additionally flags missing shift-share diagnostics (Goldsmith-Pinkham et al. 2020) and result fragility (3 of 22 SOC groups drive the finding). Resolution required: Address MUST items from editor decision letter. Re-run peer review after revision.
- Core AI-and-labor papers cited (Acemoglu & Restrepo 2020, Webb 2020, Felten et al. 2023, Eloundou et al. 2023) - Shift-share methodology covered (Goldsmith-Pinkham et al. 2020, Borusyak et al. 2022, Adao et al. 2019) - Deduction: -8 for missing Brynjolfsson et al. (2025) augmentation evidence; -7 for no engagement with Noy & Zhang (2023) or Peng et al. (2023) experimental results showing productivity gains
- CPS monthly files linked correctly; occupation crosswalks documented - O*NET task content mapped to SOC codes with version control - GPT capability scoring methodology documented and replicable - Deduction: -6 for no discussion of CPS self-employment exclusion bias; -5 for GPT self-assessment circularity not addressed
- Scripts numbered and structured (01_build_exposure, 02_build_panel, 03_estimate, 04_figures, 05_tables) - Reproducible: set.seed(20250301) at top, here() for all paths - Deductions: -10 for missing Adao et al. (2019) SEs in shift-share estimation (code-strategy alignment); -5 for no RDS checkpoint after exposure index construction (takes 45 min with GPT API calls); -3 for figure title inside ggplot (INV-12)
- Clean LaTeX, booktabs throughout, threeparttable on all tables - Notation consistent (E_ot for employment, alpha_o for exposure, tau for treatment effect) - Deductions: -8 for abstract at 168 words (exceeds 150-word invariant INV-5); -5 for inconsistent use of "effect" vs. "association" — Section 5 uses causal language but Section 6 hedges with correlational language for the same estimates
- All scripts run from project root - No absolute paths, no prohibited functions - CPS extract code included with IPUMS variable list - O*NET version and download date documented - README includes GPT API model version (gpt-4-0613) and expected cost ($12.40 for task scoring)
The two critical issues are linked. Decomposing the exposure index (Issue 1) will likely also address the pre-trends concern (Issue 2) — if the substitution component shows a clean break at 2022Q4 while the complementarity component shows the pre-trend, the paper's identification is rescued and the finding becomes more interesting.
Estimated post-fix aggregate: 85-91/100 (clears commit gate, potentially clears PR gate depending on how decomposition affects referee scores).
- strategist + strategist-critic: Strike 0 of 3 (first review round) - writer + writer-critic: Strike 0 of 3 - No escalations triggered