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 deciding whether an anthropology project fits American Anthropologist (AA) and which section to target. AA is the four-field AAA flagship, so the test is significance across anthropology (sociocultural, archaeology, biological, linguistic), not single-subfield novelty. Helps frame the question and pick a section; it does not collect data or conduct fieldwork.
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。
AA is the flagship four-field journal of the American Anthropological Association. The bar is not "new to my subfield" — it is "matters to anthropology broadly." A great paper makes a finding in sociocultural, archaeological, biological, or linguistic anthropology legible and useful to readers in the other fields. Use this skill to pressure-test fit and section before you invest.
A strong AA paper usually clears all four:
amanthro-research-design).amanthro-transparency-and-data).| Home subfield | Reach to four-field anthropology by… |
|---|---|
| Sociocultural | draw the portable concept/mechanism, not just the ethnographic particular; speak to theory others can use |
| Archaeology | connect material/temporal evidence to general claims about social life, not only site chronology |
| Biological / physical | tie human-variation or evolutionary findings to social and cultural meaning, not biology alone |
| Linguistic | show what the language data reveal about culture, power, or cognition beyond the grammar |
| Reflexive / public | make the methodological/ethical/political stake one the whole discipline shares |
amanthro-tables-figures).【Question】one sentence
【Four-field reach】who outside the subfield cares, and why
【Contribution type】concept / reframing / decisive case / corrected record
【Section】Research Article / Vital Topics / World / Public / Multimodal / Review Essay
【Ethics flag】any consent / community-accountability / heritage issue to design for now?
【Fit verdict】strong / needs reframing / off-fit (why)
【Next】amanthro-literature-positioning
../../resources/external_tools.md — data/archives/lab sources by subfield../../resources/official-source-map.md — AA sections and scope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。