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.
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Use when building the actual theory for an Academy of Management Review (AMR) manuscript — turning a positioned puzzle into defined constructs, explicit relationships, propositions, and boundary conditions. Constructs the theory; it does NOT stress-test the argument's logic (that is amr-data-analysis) or design any data collection (AMR has none).
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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 theory is assembled in a disciplined sequence. Skipping a step is the most common reason a draft reads as "assertions, not theory."
amr-data-analysis.)For each construct, fill:
Oliver's "Strategic Responses to Institutional Processes" (AMR 1991, DOI 10.5465/amr.1991.4279002) builds a typology of five responses (acquiescence, compromise, avoidance, defiance, manipulation) and derives propositions linking antecedent conditions (cause, constituents, content, control, context) to them — defined constructs, explicit mechanisms, numbered propositions, stated boundaries, and not a single datum.
【Constructs】[name: definition / domain / distinction / level] for each
【Assumptions】[explicit list]
【Relationships】[construct → construct : form + mechanism]
【Propositions】P1...Pn (each with one-line mechanism)
【Boundary conditions】where the theory holds / fails
【Next step】amr-methods (refine construction) → amr-data-analysis (logic check)
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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.
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.
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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.
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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.
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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.
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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.
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