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 you need to understand the Academy of Management Review (AMR) developmental, multi-round review and interpret a decision letter focused on theoretical novelty and logical soundness. Explains the process and decision types; it does NOT draft the response document (that is amr-rebuttal).
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。
AMR uses a double-anonymous, multi-round, developmental review. "Developmental" is the operative word: the goal is to help promising theory papers become stronger, so reviews are detailed and the bar rises across rounds. Verify current specifics on the official page, but the durable shape is:
The editor screens for fit and contribution before sending a paper out. Most common AMR desk rejects: (1) "this is an empirical paper"; (2) incremental theory; (3) a literature review with no original move; (4) construct proliferation (relabeling); (5) untestable-in-principle or tautological propositions; (6) poor fit/scope or anonymization failures.
| Signal in the letter | What it usually means | Your move |
|---|---|---|
| "Promising but the contribution is not yet clear" | Theory may be sound but under-differentiated | Re-run amr-contribution-framing |
| "Propositions are not well supported by the argument" | Logic gaps between premises and propositions | Re-run amr-data-analysis, then amr-theory-development |
| "Reads as a review of the literature" | No real theoretical move | Re-run amr-literature-positioning + amr-writing-style |
| "Constructs overlap existing ones" | Construct distinctiveness challenged | Re-run amr-theory-development (domain/distinction) |
| "Scope/level inconsistency" | Level-of-theory confusion | Re-run amr-methods (cross-level) |
| Editor highlights specific reviewer points as essential | The editor's priorities | Address these first and most fully |
amr-rebuttal).【Decision type】Reject / Reject & Resubmit / Major (R&R) / Minor
【Editor's priorities】ranked list
【Comment triage】substance: [...] | presentation: [...]
【Skill mapping】comment → amr-* skill
【Reviewer conflicts】[...]
【Go / no-go】pursue revision? rationale
【Next step】amr-rebuttal
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。