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 revising the prose, abstract, and introduction of an American Economic Journal: Applied Economics (AEJ: Applied) manuscript so the design and headline estimate land in the first paragraph in AEA house style. Polishes exposition; it does not change the result (aeja-identification / aeja-robustness) or the exhibits.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
AEJ: Applied prose is plain, direct, and design-forward. The introduction should follow the applied-micro arc: question → why credible identification is hard → the design that solves it → headline causal estimate with a standard error → mechanism → policy/economic lesson → brief roadmap. A non-specialist applied economist should grasp the question and the answer in the first paragraph; the design should be legible by the end of the second.
| Section | What it must do at AEJ: Applied |
|---|---|
| Abstract | question + design + headline estimate (with units & SE) + lesson, in ≤ the AEA word norm |
| Intro ¶1 | the economic question and the headline number, with uncertainty, no throat-clearing |
| Intro ¶2–3 | why naive estimates fail + the source of variation that fixes it |
| Intro mid | the estimate, the mechanism, and the magnitude's economic meaning |
| Intro end | contribution (honest delta), scope of the claim, brief roadmap |
aeja-literature-positioning)A first-draft intro opens with a page of institutional background on a transit program, then a TWFE
specification, then "the effect is statistically significant." The AEJ: Applied rewrite leads with the
answer: "Does cheap transportation help teenagers find work? Using a staggered municipal rollout of free
bus passes, we find the program raised teen employment by 3.1 percentage points (s.e. 0.9)." The design is
named in the next sentence, the mechanism and the calibrated scope follow, and the institutional detail
moves to Section 2. The full before→after is in
../../resources/worked-examples/01-introduction.md.
【Abstract】question + design + estimate(units,SE) + lesson within word norm? [Y/N]
【Intro ¶1】headline number + uncertainty up front? [Y/N]
【Identification legible by ¶3】[Y/N]
【Magnitudes in prose】units + SEs in sentences? [Y/N]
【Claim calibrated】estimand + scope stated, no overclaim? [Y/N]
【Next step】aeja-replication-package
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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.
中文简介正在准备中。当前显示原文说明。
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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