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.
日本語の概要は準備中です。原文の説明を表示しています。
Use when stress-testing the LOGIC of an Academy of Management Review (AMR) theory manuscript — checking logical coherence, running thought experiments and counterfactuals, addressing alternative explanations and disconfirming cases, and verifying each proposition follows from its argument. This is ARGUMENT DEVELOPMENT, NOT data analysis; AMR publishes no datasets, no statistics, and no empirical results.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
AMR publishes NO empirical data. There is nothing to estimate, plot, or test. The "analysis" in an AMR paper is the analysis of the argument itself: does each proposition follow logically from the constructs and mechanisms? At AMR, logical soundness plays the role that statistical rigor plays at empirical journals.
This skill replaces an empirical "identification + robustness" stage. The mapping:
| Empirical sibling (AMJ/ASQ/SMJ) | AMR theory analog |
|---|---|
| Identification strategy (IV, DiD, RD, matching) | Generative mechanism — the why (Whetten 1989, DOI 10.5465/amr.1989.4308371) |
| Robustness checks / alternative specifications | Internal consistency + counterfactual probes on premises |
| Ruling out confounders | Engaging and bettering the strongest rival theory |
| Replication package (data + code) | Transparent reasoning — premises and derivations a reader can re-derive |
| "Estimates are significant and robust" | Propositions are falsifiable in principle (AMR's "testable knowledge-based claims") |
There is no instrument, no parallel-trends test, no placebo here; their presence signals a misfiled empirical paper.
Run every proposition through these before drafting.
For each Pn, write the chain explicitly: premise → premise → mechanism → conclusion. If any step is missing, the proposition is asserted, not derived. Use a Toulmin frame: claim / grounds / warrant / backing / rebuttal. The warrant (the mechanism that licenses the inference) is where most theory papers are thin.
Manipulate the focal construct in your head and trace the consequence: "If construct X
rose sharply while everything else held, what does the theory predict for Y, and is that
prediction sensible?" Then run the counterfactual: "Under what condition would X move and
Y not follow?" If the counterfactual is plausible and unexplained, you are missing a
boundary condition (route back to amr-theory-development).
For each proposition, name the strongest rival theoretical account of the same relationship. Then either (a) show why your mechanism is more complete/parsimonious, or (b) integrate the rival as a boundary condition. Ignoring rivals is the fastest path to a reject — reviewers are the rival theorists.
Actively look for a case where the proposition should fail. A theory that "explains everything" explains nothing. Either the disconfirming case is covered by a stated boundary condition, or the proposition needs to be narrowed.
Exemplar: Oliver (AMR 1991, DOI 10.5465/amr.1991.4279002) "analyzes" by argument — deriving a typology and propositions from antecedent conditions and addressing why organizations might resist rather than conform (the rival expectation) — all logic, no data.
【Per-proposition logic】P1: chain ok? / gap at warrant? ... Pn
【Thought experiments run】[focal construct → predicted consequence]
【Counterfactuals → boundary conditions】[...]
【Alternative explanations handled】[rival → resolution]
【Disconfirming cases】[case → covered by boundary / narrow proposition]
【Coherence】consistent / non-circular / sufficient / parsimonious : pass/fix
【Next step】amr-contribution-framing
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。