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 sharpening the theoretical contribution and the "so what" for an Administrative Science Quarterly (ASQ) manuscript once results exist. Frames the contribution; it does not develop the underlying theory or run analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
At ASQ the deliverable is a change in how organization theorists understand something. A finding answers a question; a contribution reframes the question or overturns a prior. Frame around the surprise identified in asq-topic-selection and the mechanism from asq-theory-development. The ASQ archetype is a paper that gives the field a new lens it cannot un-see — the garbage-can model of organizational choice (Cohen, March & Olsen 1972, ASQ) or Barley's (1986, ASQ) reconception of technology as an occasion for structuring. Note what is not the ASQ deliverable: an improvement in firm performance (the SMJ register) or a well-powered confirmation of a moderated hypothesis (a common AMJ profile) is not, by itself, a theoretical contribution here.
Avoid the weakest "contribution" types ASQ tends to reject: "we replicate prior work in a new setting," "we add a moderator," "we study an understudied topic."
Place a sharp statement early (intro) and revisit it in the discussion. Shape:
"We contribute to [conversation] by showing [surprising insight]. Whereas prior work assumed [prior], we demonstrate [reframing/mechanism/boundary], driven by [mechanism]. This implies [non-obvious consequence] and reopens [new question]."
【Contribution (one sentence)】...
【Type】reframing / mechanism / boundary / integration / new model
【Prior overturned】what the field assumed
【Generativity】new questions opened
【Claim calibration】matches evidence? (yes/no)
【Next step】asq-tables-figures
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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.