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 appraising the cumulative evidence and ensuring balance in an Academy of Management Annals (Annals) review — weighing conflicting findings by credibility, steelmanning rival schools, and handling the author's own work even-handedly. Audits evidence quality and fairness; it does not design the framework (amann-organizing-framework) or build exhibits (amann-tables-figures).
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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。
An Annals review carries no estimates of its own — but it is not therefore neutral. The "attitude" is disciplined critical appraisal: you judge how good the cumulative evidence is, reconcile conflicts by credibility, and state where the field's confidence is and is not warranted. This is the review-craft replacement for primary-research robustness checks: you are appraising other people's designs, not defending your own.
A review must be comprehensive in coverage yet selective in emphasis — long (~50 pages) but not an inventory. Resolve the tension by tiering the corpus:
| Tier | Treatment |
|---|---|
| Foundational / field-defining | discussed in text — what it established and its limits |
| Important contributions | grouped and weighed within the framework's cells; cited with their finding |
| Confirmatory / incremental | cited in clusters ("see also …") to show coverage without bloating prose |
| Tangential | cited only where it bears on a specific claim |
Comprehensiveness is proven by the citation set (the saturation log from amann-literature-synthesis); selectivity is exercised in the prose. Equal-length summaries of every paper abdicate the editorial judgment that is the review's value.
Management findings conflict constantly. Reconcile them by why they differ, never by tally:
Occasionally a credibility judgment cannot be settled by reading alone: the review's account of a controversy hinges on whether a staggered-adoption TWFE estimate survives modern corrections, or an apparent consensus may melt once publication bias is priced in. When a load-bearing magnitude comes with a replication package, audit it rather than adjudicate by prose — the shared playbook execution-with-mcp maps each design family to callable StatsPAI / Stata MCP tools (bacon_decomposition to expose bad-comparison weighting, callaway_santanna to re-estimate, honest_did_from_result for pre-trend fragility). Any number produced this way must come from an actual run and be labelled as the review's own re-analysis. This is the exception, not the Annals default: most appraisal here stays qualitative.
Annals is the review-of-the-field: its account of a debate becomes the field's shared reference, and the surveyed authors often referee the review. Balance is therefore both ethical and strategic.
【Tiering】corpus split foundational/important/confirmatory/tangential? Y/N
【Comprehensiveness evidence】saturation log + citation set support "nothing important missing"? Y/N
【Conflict handling】reconciled by credibility + construct + context (not vote-count)? Y/N
【Evidence quality】strong/mixed/thin stated per claim? Y/N
【Steelman】each rival school stated at its strongest? Y/N
【Self-citation audit】own work at warranted tier; emphasis identity-blind? Y/N
【Attitude labelled】provocative reads marked as author's judgment? Y/N
【Next skill】→ amann-tables-figures (who-found-what tables + framework figure)
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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.
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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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