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.
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Use when deciding whether a project is the ONE crisp, important, well-identified insight that an American Economic Review: Insights (AER: Insights) short-format manuscript requires, rather than a multi-result paper for AER or a field paper for the AEJ family. Tests single-insight fit; it does not run the analysis.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
AER: Insights publishes short, self-contained papers built around a single important, well-executed idea. The decision is not "is this good?" but "is there exactly ONE crisp, surprising, well-identified insight that can stand alone in a short paper at AER-level importance?" Three filters:
| Symptom | Better venue |
|---|---|
| Multiple inter-dependent results that need each other | AER (long, multi-result flagship) |
| One result but field-specific, not general interest | AEJ: Applied / Micro / Macro / Policy |
| Incremental / a robustness note on existing work | a field journal or a comment |
| Pure methods with no substantive payoff in a short paper | a methods/econometrics journal |
aeri-identification) — a clean insight with a shaky design is not publishable short, because there is no room to defend a weak design.| Archetype | The single insight is… | Failure mode |
|---|---|---|
| One clean causal estimate | a single headline effect from a credible design | reporting every heterogeneity split in-text |
| A striking new fact / measurement | one important fact a non-specialist did not know | a descriptive paper with no single takeaway |
| A sharp theory result | one surprising theorem / impossibility | a general framework with the result as a corollary |
| A method that flips an answer | the substantive answer it changes, in few pages | the method shown without a substantive payoff |
A fictional project has (a) a clean RCT effect of posted wages on women's applications, (b) a heterogeneity split by occupation, and (c) a structural extension on wage offers. The AER: Insights paper is (a) alone — one surprising, general-interest, cleanly identified effect. (b) and (c) go to the Supplemental Appendix or a separate paper. Compressing all three into one short paper would be a "mini-AER" and likely a reject; the discipline is to make (a) the entire paper and defend it superbly in few pages.
If you cannot pass all three filters — single insight, AER-level importance, self-containment — the honest
move is to route elsewhere (AER for multi-result, an AEJ for field interest) rather than force-fit. The
fast, no-R&R process (aeri-referee-strategy) punishes a borderline
fit with a quick reject.
【One-sentence insight】<single headline object>
【Importance】why a non-specialist cares (general interest, AER-level)
【Prior overturned】<belief the result corrects>
【Fits short format?】core argument under cap, rest to appendix? [Y/N]
【Venue check】AER: Insights vs AER vs AEJ — chosen because […]
【Next step】aeri-literature-positioning
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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.
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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.
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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.
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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.
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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.
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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.
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