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 of an Accounting, Organizations and Society (AOS) manuscript — choosing a social/organizational theory tradition (institutional theory, governmentality, Bourdieu, actor-network theory, practice theory, psychological process theory) and making it generate the analysis rather than decorate it. Builds the lens; it does not collect data (aos-methods) or frame the contribution (aos-contribution-framing).
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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。
At AOS the theory is not a motivation ritual — it is the product. Empirics earn publication by extending, complicating, or overturning a way of understanding accounting's social operation. Before drafting anything else, answer: which conversation about how accounting works in organizations and society will this paper change, and through which conceptual apparatus?
Choose one tradition and inhabit it; AOS referees can tell a user of a theory from a citer of one.
Before committing to a tradition, you should be able to (a) summarize its two or three primary sources from memory, (b) name one AOS paper that used it well and one that used it thinly, and (c) say what the tradition cannot explain. Failing (c) is the tell of a citer rather than a user.
【Tradition】institutional / governmentality / Bourdieu / ANT / practice / psychological-process
【Conceptual innovation】one sentence ...
【Core concepts】each concept → where it will work in the findings ...
【Expectations & surprises】what the lens predicts; what would count as an anomaly ...
【Process (if experimental)】mediator / moderator / boundary condition ...
【Next step】aos-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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