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。
Use when building exhibits for an Academy of Management Annals (Annals) review — the signature framework figure, who-studied-what synthesis tables, and search/coverage exhibits that summarize across a literature. Designs review exhibits; it does not produce original-estimate regression tables (an Annals review reports no new data of its own).
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。
Annals exhibits summarize and reorganize across the literature; they never present the author's own estimation. The workhorses:
| Exhibit | Purpose | Design notes |
|---|---|---|
| Framework figure | render the organizing spine — typology grid, process model, multi-level architecture | this is usually the review's signature exhibit; it should be restate-able from memory and carry the "new way of seeing the field" |
| Who-studied-what synthesis table | one row per study (or per stream): construct, theory base, method/context, finding, your appraisal | rows ordered by the framework's cells, not chronology; columns let the reader compare comparable work |
| Search / coverage exhibit | a PRISMA-style flow, a search-terms table, or a publications-over-time chart | documents the systematic process from amann-literature-synthesis and pre-empts "why did you omit X?" |
| Agenda / gap matrix | the framework's empty cells laid out as the future-research agenda | turns the generativity of the framework into a concrete, visible roadmap |
amann-literature-synthesis).amann-evidence-standards).If you assemble effect sizes into a quantitative synthesis (forest plot, meta-regression) or a co-citation map, you are doing meta-analysis or bibliometrics, with all their assumptions — comparable constructs, independence, publication-bias diagnostics, weighting. Do this only where the literature genuinely supports it; a spurious pooled magnitude or an over-interpreted co-citation cluster is less honest than a qualitative synthesis table. Annals readers include the methodologists who would catch invalid pooling.
【Exhibit set】<framework figure / synthesis tables / coverage exhibit / agenda matrix>
【Framework figure】encodes the spine + "new way of seeing"; restate-able from memory? Y/N
【Synthesis table】rows by framework cell; comparable constructs only? Y/N
【Appraisal column】present for every finding? Y/N
【Coverage exhibit】documents the systematic search (PRISMA-style)? Y/N
【Meta/bibliometric】pools only commensurable work (or omitted)? Y/N
【Sourcing】every cell traces to the coding matrix? Y/N
【Next skill】→ amann-writing-style (weave exhibits into the synthesis prose)
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。
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。
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。
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。
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。
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。
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。