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 articulating what an Accounting, Organizations and Society (AOS) manuscript contributes — converting findings into a claim that changes how the field understands accounting's social, organizational, behavioral or institutional operation. Frames the payoff; it does not produce new analysis (aos-data-analysis).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
The currency is conceptual: after your paper, readers should think differently about how accounting operates in organizations or society. Empirical novelty, clean identification, or an exotic setting are inputs, not contributions. Strong AOS contribution types:
| Type | Shape of the claim |
|---|---|
| New concept | Names and demonstrates a phenomenon the field lacked words for |
| Mechanism | Shows how an accounting practice produces its organizational/social effect |
| Boundary condition | Shows where a received account holds, fails, or reverses |
| Reconceptualization | Recasts a familiar practice (budgeting, audit, risk metrics) as doing different work than assumed |
| Process evidence | (Experimental) locates the psychological process behind a known accounting effect |
| Genealogy | Shows how a present practice became possible and what it forecloses |
aos-literature-positioning: the contribution is the answer to that specific disagreement, not a free-floating insight.Read your contribution sentence to a colleague who knows the AOS canon but not your project. If their first question is "what was the effect size?" you framed an empirical claim; if it is "how does that square with [canonical paper]?" you framed a conceptual one — the AOS kind.
【Contribution】one sentence: practice / effect / mechanism / conversation changed ...
【Type】concept / mechanism / boundary / reconceptualization / process / genealogy
【Concessions owed】nearest paper 1 / 2 / 3 must now ...
【Give-back】what the imported theory gains ...
【Scope】what travels, through what logic; what stays local ...
【Next step】aos-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.