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 deciding whether a project fits AEJ: Economic Policy rather than J. Public Economics, AEJ: Applied, or AER, and when sharpening the policy question and its welfare stake for an AEJ: Economic Policy manuscript. Frames fit and the policy-question-first contribution; it does not design the identification or run estimation.
中文简介正在准备中。当前显示原文说明。
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: Policy publishes the economic analysis OF policy: a paper is built around a policy question ("should this tax / mandate / subsidy / regulation exist, expand, or change, and at what welfare cost?") whose answer carries a welfare, cost-benefit, or distributional implication of broad interest to the AEA readership. Two halves must both be present from the first page:
Public economics & taxation · environmental & energy · health · education · labor & social insurance · regulation & antitrust · development policy · political economy of policy. Empirical (quasi-experimental / RCT) and applied-theory work both fit — provided the policy question and welfare relevance are explicit.
| If the paper is mainly… | It belongs at… | Tell |
|---|---|---|
| broad-interest policy question + credible causal evidence + welfare reading | AEJ: Policy | the policy lesson is the headline |
| a deep field-public-finance contribution for specialists | J. Public Economics | broad readership would not follow the "so what" |
| identification-driven applied micro with no policy lever / welfare claim | AEJ: Applied | the natural experiment, not a policy, is the point |
| a first-order, general-interest result warranting top-5 length | AER | the contribution is larger and longer than a field-leading policy paper |
Before investing in a draft, answer these in one sentence each; a "no" or "I can't" on any is a fit problem:
A draft estimates that a state's expansion of a childcare subsidy raised maternal employment. As "we find subsidy → employment" it is a clean applied-micro result (AEJ: Applied). Reframed for AEJ: Policy: "Is expanding the childcare subsidy a cost-effective way to raise maternal labor supply, and who bears the cost?" — now the employment elasticity feeds a cost-per-additional-worker and an incidence split across income groups (illustrative), and the paper has a policy lever, a counterfactual, and a welfare reading.
【Policy lever】instrument + affected population (one sentence)
【Policy question】stated as a question with a welfare/cost-benefit/distributional stake
【Counterfactual】what the policy is compared against
【Welfare object to report】MVPF / cost-per-X / incidence / calibrated welfare
【Fit verdict】AEJ: Policy vs JPubE / AEJ:Applied / AER + one-line reason
【Next step】aejpol-literature-positioning
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。