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 building the conceptual exhibits of an Academy of Management Review (AMR) manuscript — process models, 2x2 typologies, multi-level frameworks, and propositions tables that carry theoretical work. These are CONCEPTUAL figures, NOT data plots; AMR has no charts of results because it has no data.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
AMR exhibits do theoretical work. There are no scatterplots, bar charts, or regression tables — there is no data. Every figure must be readable directly off the theory: each box is a defined construct, each arrow is a stated proposition or mechanism.
| Exhibit | Use it when | It must show |
|---|---|---|
| Process model | The theory is a sequence / becoming | Stages, triggers, feedback loops, timing — and the engine that drives movement |
| 2x2 (or NxM) typology | The phenomenon has theoretically distinct types | Two theory-derived dimensions; mutually exclusive, jointly exhaustive cells; ideally theorized transitions between cells (cf. Oliver's antecedent-conditions × responses logic, AMR 1991, DOI 10.5465/amr.1991.4279002) |
| Multi-level framework | The action spans individual ↔ collective | Levels as horizontal bands; emergence (bottom-up) and top-down arrows labeled |
| Variance/relational model | Core constructs and their specified links | Constructs as nodes; arrows labeled with proposition numbers and form (causal/moderating/mediating) |
| Propositions table | There are many propositions | Pn |
| Empirical sibling exhibit (AMJ/ASQ/SMJ) | AMR conceptual replacement |
|---|---|
| Table 1: descriptive statistics / correlations | Construct-definition table (definitions + scope conditions) |
| Regression / coefficient table | Propositions table (P1...Pn) |
| Marginal-effects / interaction plot | Conceptual model figure with moderator arrows |
| Event-study / time-series plot | Process model with stages and feedback loops |
If any exhibit reports a number estimated from data, the manuscript is a misfiled empirical paper. A construct-definition table also supports construct clarity (Suddaby, AMR 2010, DOI 10.5465/amr.2010.0419).
| # | Proposition (constructs + form) | Mechanism (why) | Boundary condition |
|----|--------------------------------------------------|-------------------------|--------------------|
| P1 | The stronger A, the more likely B | M1: ... | holds when C |
| P2 | A's effect on B is amplified by D | M2: ... | weakens when ... |
【Exhibit type】process / typology / multi-level / relational + propositions table
【Constructs shown】all defined in text? yes/no
【Arrows】each maps to Pn or mechanism? yes/no
【Adds beyond text?】shows structure / merely repeats (fix)
【Format check】meets AOM figure guidelines (verify)
【Next step】amr-writing-style
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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.