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 defending the research design of an American Anthropologist (AA) manuscript — fieldwork and participant observation, interview and archival design for ethnographic/qualitative work, material/contextual analysis for archaeology, and appropriate lab/quantitative design for biological anthropology. AA judges each mode on its own terms with reflexivity. Strengthens the design; it does not write code or run fieldwork.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
AA accepts many methodologies but is demanding about each. The design must credibly connect the
argument (amanthro-theory-building) to evidence — and at AA, ethnographic and qualitative inference
is first-class, not a soft substitute for statistics. This skill is mode-aware: pick the section that
matches your work and defend it against the strongest alternative reading, reflexively and ethically.
amanthro-literature-positioningamanthro-theory-building).amanthro-transparency-and-data for repatriation and consent).For the single strongest rival reading, write one sentence: "If the rival account were right rather than mine, the evidence would look like ___; instead it looks like ___." If you cannot, the design does not yet identify the contribution — regardless of how rich the material is.
amanthro-transparency-and-data)【Mode】ethnographic / archival-material / biological-quant / multimodal-participatory
【Evidence base】sites/interlocutors/assemblage/sample + how selected
【Key assumption(s)】and how each is defended (incl. reflexivity)
【Rival ruled out】the adjudication sentence
【Disconfirmation】what would have shown you wrong
【Ethics designed in】consent / anonymization / accountability flagged? [Y/N]
【Next】amanthro-data-analysis
../../resources/external_tools.md — CAQDAS, archival tools, and lab/quant packages by subfield../../resources/official-source-map.md — AA scope and ethics/transparency notes아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.