本文へ移動
cccskills
無料GitHub で公開

aos-contribution-framing

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).

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Contribution Framing (aos-contribution-framing)

When to trigger

  • Findings are solid but the discussion section restates them instead of arguing from them
  • The current contribution reads as "first evidence from setting X" or "extends literature Y"
  • You cannot answer, in one sentence, what a reader of the AOS canon now understands differently
  • A reviewer asks what the paper offers beyond the case / beyond the experiment

What counts as a contribution at AOS

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:

TypeShape of the claim
New conceptNames and demonstrates a phenomenon the field lacked words for
MechanismShows how an accounting practice produces its organizational/social effect
Boundary conditionShows where a received account holds, fails, or reverses
ReconceptualizationRecasts 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
GenealogyShows how a present practice became possible and what it forecloses

Build the claim from the findings

  • Draft the contribution as one falsifiable-in-spirit sentence: "This paper shows that [accounting practice] [does what] through [mechanism/process], which [changes what] in [which conversation]."
  • Tie it to the crack identified in aos-literature-positioning: the contribution is the answer to that specific disagreement, not a free-floating insight.
  • Test transferability honestly: what about the claim travels beyond the site or the lab, and via what logic (theoretical generalization for field work; process generality for experiments)? Say so explicitly — overselling a single case is as damaging as underselling it.
  • Locate the implications where AOS expects them: for theory first; for practice, regulation, or policy second and only as far as the evidence carries.

The discussion section that delivers it

  1. Restate the puzzle and the answer in two or three sentences — conceptual language, not variable names.
  2. Spell out what each nearest prior paper must now concede or refine.
  3. Develop the theoretical implications: what the lens itself gains from the case (AOS values papers that give back to the imported theory, not just borrow from it).
  4. Bound the claim: scope conditions, site particularities, design limits — stated as analysis, not ritual humility.
  5. Open genuine next questions the reconceptualization makes visible.

Quick self-test

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.

Checklist

  • Contribution stated in one sentence, conceptual in kind
  • Claim answers the specific crack the front end opened
  • Each nearest prior paper's required revision is named
  • Transferability argued through theory/process, not asserted through sample
  • The imported theory receives something back
  • Practice/policy implications sized to the evidence

Anti-patterns

  • Inventory framing: "we contribute to three literatures" followed by lists.
  • Setting-as-payoff: the exotic case presented as the point rather than the lens's occasion.
  • Managerialism: converting an interpretive study into consulting advice AOS never asked for.
  • Theory borrowed, nothing returned: heavy conceptual machinery with no reflection on what the case teaches the theory.
  • Contribution inflation: claims the evidence register or the design cannot back.

Output format

【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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

brycewang-stanford のスキルをすべて見る

このスキルの問題を報告する