alterlab-text-as-data
De la autor
Analyzes text as social-science data — topic modeling (BERTopic with embeddings + class-based TF-IDF, LDA/NMF via scikit-learn or gensim), document embeddings (sentence-transformers), dictionary/lexicon methods, and supervised text classification — choosing the method that matches the inferential goal (discovery vs measurement vs prediction) and validating topic reliability rather than trusting one stochastic run. It uses the verified stack (BERTopic, scikit-learn, gensim CoherenceModel, spaCy, sentence-transformers) with pinned patterns. Use when the request mentions topic modeling, text as data, computational text analysis, document embeddings, dictionary/sentiment lexicons, or classifying a corpus. For training or fine-tuning transformer models prefer alterlab-transformers; for humanities close-reading corpora prefer alterlab-digital-humanities. Part of the AlterLab Academic Skills suite.
Instalare
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-text-as-data -a claude-code
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De ce 77
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- alterlab-digital-humanitiesAlterLab-IEU/AlterLab-Academic-SkillsApplies computational methods to humanities research — text mining and NLP (LDA/BERTopic topic modeling, sentiment, named entity recognition with spaCy/NLTK), corpus linguistics (concordance, collocation, keyness), digital archives (Dublin Core, TEI XML), GIS for history, network analysis, stylometry and authorship attribution, OCR (Tesseract, Kraken, Transkribus), and data visualization (Gephi, Palladio). Use when distant-reading a literary corpus, mapping historical events or trade networks, attributing disputed authorship, digitizing historical documents, or building digital scholarly editions. Part of the AlterLab Academic Skills suite.Știință★ 78
- alterlab-social-science-methodsAlterLab-IEU/AlterLab-Academic-SkillsGuides advanced social science research methods — discourse analysis (Fairclough CDA, Gee), conversation analysis, quantitative content analysis, Qualitative Comparative Analysis (QCA), process tracing, archival research, participatory/community-based research (PAR, CBPR), Delphi and Q methodology, social network analysis (SNA), bibliometrics and scientometrics, systematic mapping reviews, and program/policy evaluation. Use when designing or conducting any of these studies — choosing a specialized method, building a coding scheme, establishing causal mechanisms in case studies, mapping relational or subjective data, or applying an evaluation framework. For interpretive coding (thematic analysis, grounded theory, IPA, ethnography) use alterlab-qualitative-methods; for qual+quant integration use alterlab-mixed-methods; to run QCA, SNA, or text-as-data analyses on data use alterlab-qca, alterlab-sna, or alterlab-text-as-data. Part of the AlterLab Academic Skills suite.Știință★ 78
- alterlab-hypothesis-genAlterLab-IEU/AlterLab-Academic-SkillsFormulates structured, testable hypotheses from experimental observations using a scientific-method framework — derives predictions, proposes mechanisms, and designs experiments to test them, then renders a LaTeX report. Use when turning observations or data into falsifiable, mechanistic hypotheses, competing-explanation sets, testable predictions, or experimental designs. For a standalone systematic literature review or evidence synthesis (not a means to hypotheses) use alterlab-literature-review; for open-ended brainstorming before any specific observation use alterlab-scientific-brainstorm; to evaluate an existing manuscript use alterlab-peer-review. Part of the AlterLab Academic Skills suite.Testare★ 78
- alterlab-qualitative-analysisAlterLab-IEU/AlterLab-Academic-SkillsAnalyzes qualitative data as a dispatched pipeline module — codebook development, thematic / framework / content analysis, and inter-coder reliability computed correctly (Krippendorff's alpha as primary via the krippendorff package or a bundled stdlib nominal calculator with bootstrap CIs; Cohen's / Fleiss' kappa via statsmodels) with 95% CIs and thresholds (alpha >= .80 reliable, .667-.80 tentative). It BRANCHES by design: coefficient-based ICR for codebook / content-analytic coding, versus consensus-and-reflexivity for reflexive thematic analysis where a statistic is not the right criterion. Supports human-vs-LLM double-coding with an alpha check against a human gold standard. Use when coding interviews or open-ended text, building a codebook, or reporting intercoder reliability. For topic modeling / embeddings / supervised text classification prefer alterlab-text-as-data; for the reflexivity gate prefer alterlab-ssci-reflexivity-gate. Part of the AlterLab Academic Skills suite.Design★ 78
- alterlab-literature-reviewAlterLab-IEU/AlterLab-Academic-SkillsConducts comprehensive, systematic literature reviews across multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar), with PRISMA 2020 flow tracking, study screening (title/abstract and full-text), evidence-table extraction, and risk-of-bias assessment (RoB 2, ROBINS-I, AMSTAR 2), producing professionally formatted markdown documents and PDFs with verified citations in multiple styles (APA, Nature, Vancouver). Use when running a systematic or scoping literature review, research synthesis, or broad literature search, building a PRISMA flow diagram, screening studies, extracting an evidence table, or assessing risk of bias across biomedical, scientific, and technical domains. For quantitative pooling of effect sizes (forest/funnel plots) use alterlab-meta-analysis; for an open-ended multi-agent investigation, fact-check, or Socratic research-question work use alterlab-deep-research. Part of the AlterLab Academic Skills suite.Inginerie★ 78
- alterlab-snaAlterLab-IEU/AlterLab-Academic-SkillsApplies social-network-analysis method discipline to relational data — degree/betweenness/closeness/eigenvector centrality and PageRank, community detection (Louvain, greedy-modularity, and — from networkx 3.7 — Leiden native in networkx; igraph/leidenalg for large graphs), and inferential network models (ERGM) — choosing the measure that matches the substantive question and the right dependence assumptions, then routing computation to the existing networkx (and igraph/R) tooling. Use when the request mentions social network analysis, centrality, key players/brokerage, community or cluster detection in a network, ERGM, or modeling ties between nodes. For general graph algorithms and plotting prefer alterlab-networkx; for graph neural networks prefer alterlab-torch-geometric. Part of the AlterLab Academic Skills suite.Altele★ 78
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