Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when building or revising the exhibits of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript so the main causal result is legible in one table or figure and respects AEA house presentation norms. Formats exhibits; it does not establish the result (aeja-identification / aeja-robustness) or write the surrounding prose.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
At AEJ: Applied the main causal estimate should be findable in seconds and every exhibit should earn its place. AEA house style permits significance stars but expects standard errors in parentheses, clear notes that make each exhibit self-contained, and clean figures over chartjunk. Lead with the design's signature visual — the event-study plot, the RD scatter, or the balance table — because at this journal the picture of the identification is half the persuasion.
| Exhibit | What it must show | Common failure |
|---|---|---|
| Main results table | headline coefficient, SE in parentheses, N, controls indicated, dependent-var mean | too many columns; no SEs; over-precision |
| Balance table (RCT) | baseline means by arm, differences, joint test | missing joint test; no attrition row |
| Event-study figure | leads + lags, CIs, reference period, flat pre-trends visible | no CIs; ambiguous reference period |
| RD figure | binned scatter + fitted lines, bandwidth, density | overfit polynomial; no density panel |
| First-stage / IV table | first-stage F, exclusion logic in notes | weak first stage hidden |
| Robustness exhibit | point-estimate stability across checks | a starred wall with no map |
Generate exhibits from the fitted result, not by retyping numbers (the usual source of
body-vs-appendix drift). Full map: execution-with-mcp.
etable (multi-model columns) or did_summary_to_latex straight from the
result_id — one variable definition, one set of numbers, body and appendix in sync.plot_from_result / enhanced_event_study_plot / event_study_table —
axis units and the SE/clustering note baked in.See a full fitted-result → exhibit chain in the JF execution walkthrough.
A draft's Table 4 has 12 columns sweeping every control combination, and the headline coefficient is buried in column 9 with only t-statistics shown. The AEJ: Applied fix: promote the preferred specification to a two-panel Table 2 — Panel A the main estimate (coefficient 3.1, s.e. 0.9 in parentheses, N, dependent-var mean 0.44), Panel B the same with the full controls — and move the sweep to the online appendix. Add Figure 1, the event-study with confidence intervals and a clearly marked reference period, so the identification is visible before the reader reaches the table. The result is now findable in seconds.
【Headline exhibit】one table/figure carrying the main estimate? [Y/N]
【Inference shown】SEs in parentheses + clustering level in notes? [Y/N]; stars defined if used
【Identification figure】event-study / RD / balance present with CIs? [Y/N]
【Self-contained notes】sample/units/clustering/controls in every note? [Y/N]
【Figure quality】vector, legible, no chartjunk? [Y/N]
【Next step】aeja-writing-style
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Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.