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 the marginal contribution of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript is fuzzy or undersold and must be staked precisely against prior applied-micro work. Positions the contribution; it does not fix the identification (aeja-identification) or the prose (aeja-writing-style).
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 contribution is judged as: a credibly identified answer to a question the applied-micro community has not yet settled, positioned against the closest prior work — not the whole field. Editors and referees reward a contribution that is specific, honest, and load-bearing. Name your contribution type, then position only against the papers that threaten it.
| Contribution type | What "new" must mean | Positioning move |
|---|---|---|
| New answer | a magnitude prior work did not credibly estimate | show why prior designs failed to identify it |
| New design / source of variation | a cleaner identification of a known question | contrast your design's assumptions vs. the standard one |
| New setting / population | external validity that changes the lesson | argue why this setting is informative, not just available |
| New mechanism | decomposing a known effect into channels | show the prior literature left the channel ambiguous |
| New measurement | a quantity nobody could measure before | establish the measure's validity, not just its novelty |
A draft claims to be "the first study of cash transfers and schooling in [country]." A referee replies that three RCTs already estimate this effect elsewhere, so geography is not a contribution. The AEJ: Applied fix is to reposition by identification and mechanism: the closest papers estimate the effect where transfers are conditional, leaving open the unconditional case; this paper exploits an unconditional rollout and shows the schooling response is half as large (illustrative), isolating the conditionality channel. The contribution becomes a mechanism the closest authors would agree they did not pin down — not a new map pin.
【Contribution type】answer / design / setting / mechanism / measurement
【Three closest papers】1) ___ (open: ___) 2) ___ (open: ___) 3) ___ (open: ___)
【Honest delta】one sentence the closest author would accept
【Positioned by】identification reason prior work left it open: ___
【Sibling check】settled by AER/AEJ:Policy/field that we do NOT re-claim: ___
【Next step】aeja-identification (or aeja-writing-style if design is settled)
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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.