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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when judging whether a research idea fits Administrative Science Quarterly (ASQ) and has the *surprising* theoretical insight ASQ demands. Sharpens the puzzle and the contribution claim; it does not design the study or write theory sections.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
ASQ publishes deep, often counterintuitive insight about organizations and the social structures around them. Since its 1956 founding (by James D. Thompson at Cornell), the journal has prized organizational ideas drawn from sociology, psychology, political science, economics, anthropology, and management — not a single home discipline. The bar is not "is this interesting?" but "does this change how we understand organizing?" A finding that merely raises firm performance (the SMJ test) or cleanly tests a hypothesis in a new sample (a common AMJ profile) is not yet an ASQ paper.
Fit test — a strong ASQ paper usually has all four:
Run the idea through these prompts before any data work:
If the best you can do is "no one has studied X in setting S," that is a gap, not a puzzle — keep digging or move to a different journal.
Decide which engine the puzzle needs — this previews asq-methods:
Do not pick method first and reverse-engineer a question. Unlike journals where the quantitative hypothesis-testing template is the default expectation, at ASQ a single deep ethnography or a historical case can be the whole paper if the insight is profound and the craft is high.
Draft the claim now, in this shape:
"We study [puzzle] in [setting]. Existing work in [conversation] expects [prior]. We show instead that [surprising insight], because [mechanism]. This reframes [construct/process] and matters for [organizational consequence]."
If you cannot fill every slot, the topic is not ready.
【Puzzle】one-sentence anomaly/tension
【ASQ fit】organizational core? (yes/no + why)
【Surprise】what prior is overturned
【Engine needed】qualitative / quantitative + why
【Contribution claim】the filled one-paragraph claim
【Next step】asq-theory-development
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.