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 theoretical engine for an Administrative Science Quarterly (ASQ) manuscript — mechanisms, process vs. variance logic, constructs, and boundary conditions. Builds the argument; it does not select methods or run analysis.
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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。
asq-topic-selection) but no theoretical machinery to explain itThis is the central choice at ASQ. Get it right before drafting.
ASQ punishes variance framing forced onto a process question: if your data show a sequence of events transforming an organization but you write it as "factor A increases outcome B," reviewers will say the theory and phenomenon are mismatched. Match the form of the theory to the form of the phenomenon. ASQ's lineage makes process and interpretive theorizing fully legitimate as the main contribution — this is a real difference from journals where the variance/hypothesis template is the implicit standard. Barley's (1986) ASQ study of CT scanners, for instance, theorized structuring as a process rather than estimating an effect of technology on structure.
A construct is not a mechanism. State the generative mechanism — the social/organizational process that produces the relationship or sequence:
A good test: could a reader predict a new, non-obvious implication from your mechanism? If not, you have a label, not a theory.
If theory is emergent (grounded), this skill still applies — but in reverse order:
asq-data-analysis)【Theory form】process / variance + why it matches the phenomenon
【Core mechanism】actors + structure + causal/processual logic
【Boundary conditions】where it holds / weakens / reverses
【Novel implication】the non-obvious prediction or insight
【Deductive only】hypotheses + derivation status
【Next step】asq-literature-positioning
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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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