Use when deciding which asq-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an Administrative Science Quarterly (ASQ) manuscript. Routes — it does not replace — the specialized skills.
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。
This is the router. It does not replace any specialized skill; it tells you which asq- skill to use at your current stage*.
Default assumption: unless the user says otherwise, treat the target as Administrative Science Quarterly (ASQ) — a top, theory-driven journal in organization theory and the sociology of organizations, founded in 1956 by James D. Thompson (first editor 1956–1973; author of Organizations in Action) and published by SAGE in association with the Samuel Curtis Johnson Graduate School of Management at Cornell University. Its motto: "Dedicated to advancing the understanding of administration through empirical investigation and theoretical analysis." ASQ's hallmark is a deep, often surprising theoretical insight about organizations, delivered with superb craft. It publishes both rigorous quantitative work and rich qualitative/inductive studies (grounded-theory, ethnographic, historical) — method follows the theoretical question. The current Editor is Beth Bechky (UC Davis; term began July 1, 2025, succeeding Christine Beckman and András Tilcsik), an ethnographer of work and occupations — a signal of ASQ's openness to inductive fieldwork. ASQ has no submission fee, is exceptionally selective (reported acceptance ~8%), uses APA references (since January 2025), and is distinctive among management journals for its standing book review section.
| Current symptom | Next skill |
|---|---|
| Idea is interesting but not yet surprising; unsure it fits ASQ | asq-topic-selection |
| Have a phenomenon but no theoretical engine / mechanism | asq-theory-development |
| Lit review reads like a list; not in dialogue with org-theory traditions | asq-literature-positioning |
| Unsure whether design should be qualitative or quantitative; rigor gaps | asq-methods |
| Have data but coding/analysis is opaque; no data-to-theory link | asq-data-analysis |
| Can't state the contribution as a "so-what" for organization theory | asq-contribution-framing |
| Tables/figures are dense or do not show the data structure (qual or quant) | asq-tables-figures |
| Prose is flat, jargon-heavy, or "variance-speak" where process is needed | asq-writing-style |
| Preparing to submit; need a preflight checklist | asq-submission |
| Want to understand ASQ's developmental review before/after submitting | asq-review-process |
| Received an R&R and need to write the response | asq-rebuttal |
asq-topic-selection — lock the puzzle and the surprising organizational insightasq-theory-development — build the theoretical engine (process or variance) before data workasq-literature-positioning — situate in organization-theory conversationsasq-methods — choose and justify design (qualitative or quantitative); set the rigor barasq-data-analysis — execute analysis and the data-to-theory linkasq-contribution-framing — sharpen the theoretical "so what" once results existasq-tables-figures — finalize data tables, data-to-theory tables, models, figuresasq-writing-style — full-manuscript prose and craft polishasq-submission — pre-submission preflightasq-review-process — understand the developmental, multi-round processasq-rebuttal — after the R&R decision
asq-writing-styleandasq-tables-figuresare late-stage polish — do not run them while the theoretical contribution is still unsettled. At ASQ, craft cannot rescue a thin idea.
asq-topic-selectionasq-theory-developmentasq-literature-positioningasq-theory-development then asq-contribution-framingasq-data-analysisasq-contribution-framingasq-submissionasq-review-process then asq-rebuttalA pure conceptual paper fits an AMR-style stack better. A hypothesis-testing study aimed purely at firm-performance outcomes with no organizational-theory surprise may belong at SMJ — at ASQ a thin contribution dressed in a strong method will not survive screening, however clean the identification.
When a draft is thrashing, score each row 0–2 (0 = broken, 1 = shaky, 2 = solid). The lowest row is your next skill — at ASQ a low "surprise" or "so-what" score outranks any polished exhibit. Craft never rescues a thin idea here.
ASQ BOTTLENECK TRIAGE score(0-2) route if lowest
surprise / puzzle ............. [ ] asq-topic-selection
theoretical engine (mechanism) [ ] asq-theory-development
dialogue w/ org-theory canon .. [ ] asq-literature-positioning
design fits the question ...... [ ] asq-methods
data -> theory link is visible [ ] asq-data-analysis
"so what" for organization theory[ ] asq-contribution-framing
exhibits show the data structure [ ] asq-tables-figures
prose is process-, not variance- [ ] asq-writing-style
-------------------------------------------------------------
RULE: fix the lowest theory row before touching any craft row.
qualitative & quantitative are equally first-class — method follows the question.
asq-theory-development and rush to data — at ASQ the contribution is theoretical, not methodological.asq-tables-figures polish exhibits before the contribution is settled.asq-rebuttal draft a response letter before the manuscript itself has been revised.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。