Use when executing and reporting the analysis for an Administrative Science Quarterly (ASQ) manuscript — qualitative coding and data-to-theory construction, or quantitative estimation and robustness. Makes the evidence-to-theory link transparent; it does not design the study (see asq-methods).
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ASQ expects readers to see how raw data became theory — its guidelines stress that helping readers understand how the research was performed and ensuring the trustworthiness of published work are explicit aims (verify at journals.sagepub.com/author-instructions/asq). Qualitative rigor is judged on its own terms here, not held to a quantitative yardstick. Make the analytic ladder visible.
asq-tables-figures).asq-tables-figures.Run the battery, don't just enumerate it. Full map:
execution-with-mcp. ASQ wants a clean causal or well-identified observational design behind an organizational-theory contribution; reduced-form estimation fits the chain below, interpretive work does not.
romano_wolf (step-down FWER) or
benjamini_hochberg — report the adjusted threshold.oster_delta / sensemakr.wild_cluster_bootstrap (few clusters), twoway_cluster / conley;
multilevel data → cluster at the right level.audit_result(result_id) lists the missing checks and the
exact suggest_function for each.etable / did_summary_to_latex from the handle — no retyped numbers.Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
【Branch】qualitative / quantitative
【Data-to-theory link】data structure / mechanism tests done
【Key evidence】power quotes or main estimates
【Robustness/trustworthiness】checks completed + gaps
【What evidence cannot show】explicit limits
【Next step】asq-contribution-framing
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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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