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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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
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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.