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 anticipating the objections referees and coeditors will raise on an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript, and calibrating desk-reject and revision odds, before submission. Pre-empts pushback and plans the review; it does not draft the post-decision response letter (use aejmac-rebuttal).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
aejmac-replication-package).| Objection a macro referee raises | Where it lands | Pre-emption |
|---|---|---|
| "The identifying assumption is not defended" (Cholesky ordering, sign set, exclusion) | identification | Argue the assumption economically; show robustness to alternatives (aejmac-identification) |
| "The shock is anticipated / endogenous" | identification | Show unpredictability from macro history; use narrative/high-frequency instruments |
| "This is a Great-Moderation / pre-ZLB artifact" | robustness | Sample splits across the breaks, baseline shown alongside (aejmac-robustness) |
| "Calibration is undisciplined / free parameters" | theory-model | Source every parameter; targeted + untargeted moments; sensitivity |
| "The counterfactual ignores the Lucas critique" | theory-model | Argue policy-invariance of estimated parameters |
| "SVAR and LP would disagree" | robustness | Run the cross-method check and show agreement |
| "Set-identified results reported as a point" | identification / tables | Report the identified set / median-target with a band |
| "Not broad enough for AEJ: Macro" | topic / positioning | Re-frame for general interest; name the audience (aejmac-topic-selection) |
| "Results not reproducible" | replication | Build the openICPSR package early, incl. simulation code |
aejmac-topic-selection / aejmac-literature-positioning / aejmac-writing-style before submitting.【Top 3 objections this paper invites】1) ... 2) ... 3) ...
【Pre-emption status】answered-in-paper / needs-work for each
【Desk-reject risk】low / medium / high + why
【Run-now vs. reserve checks】...
【Referee suggestions span】theory + empirical? [Y/N]
【Next step】aejmac-submission
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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.