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.
日本語の概要は準備中です。原文の説明を表示しています。
Use when placing the contribution against the policy-evaluation literature and sharpening it as a policy answer for an AEJ: Economic Policy manuscript. Stakes the contribution and the policy-relevant "so what"; it does not write the full introduction or design the identification.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
At AEJ: Policy the contribution is a better answer to a policy question, not a new estimate for its own sake. Position the paper on three axes and make the policy reading the headline:
aejpol-theory-model).Author–year throughout; cite the canonical methods papers behind your design (e.g., modern DID, RDD) and the key policy-evaluation papers on this and adjacent policies — referees expect you to know the policy literature, not only the econometrics.
[1][2] citations instead of AEA author–yearA draft on a carbon tax says "we estimate the emissions response." Referees note three prior estimates exist. Repositioned: the prior estimates ignore the revenue-recycling margin; this paper recovers the response and the incidence of the recycled revenue, so it can state the net welfare cost per ton abated under alternative recycling rules (illustrative) — a cost-benefit advance no prior estimate delivered. The contribution is now a policy answer, not a fourth elasticity.
A strong AEJ: Policy intro states the contribution as up to three numbered moves, at least one of them a welfare advance: (1) credibility — "we exploit [variation] that prior work could not, removing [confound]"; (2) magnitude / external validity — "we provide the policy-relevant estimate for [population/scale] where prior estimates do not travel"; (3) welfare — "we convert the effect into [MVPF / cost-per-outcome / incidence], the object a policymaker needs." Keep each to one sentence and make the welfare bullet the one a reader remembers.
【Open policy question / gap】what a decision-maker still does not know
【Closest prior work】1–3 papers + the specific limitation you address
【Contribution bullets】(a) credibility / (b) magnitude / (c) welfare-cost-benefit — ≥1 must be welfare
【Policy "so what"】one sentence a non-specialist policymaker would grasp
【Next step】aejpol-identification (design) or aejpol-theory-model (welfare mapping)
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。