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SkillsMP は nealcaren/social-data-analysis から 17 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 17 件中 17 件を表示しています。
Simulate peer review by constructing reviewer personas from Zotero sources. Identifies relevant perspectives, retrieves full texts, builds reviewer profiles, and generates focused reviews on theory/methods and findings.
原文の言語: 英語
Draft publication-ready Theory sections for sociology research. Guides structure, paragraph functions, sentence craft, and calibration based on analysis of 80 Social Problems/Social Forces articles.
原文の言語: 英語
Meta-skill for creating genre-analysis-based writing skills. Analyzes a corpus of article sections, discovers clusters, and generates complete skills with phases, cluster guides, and techniques.
原文の言語: 英語
Draft case justification sections for interview-based sociology articles. Guides cluster selection, component coverage, and calibration based on analysis of 32 Social Problems/Social Forces articles.
原文の言語: 英語
Orchestrate manuscript revision by routing feedback to specialized writing skills
原文の言語: 英語
Draft publication-ready Methods sections for interview-based sociology articles. Guides pathway selection, component coverage, and calibration based on analysis of 77 Social Problems/Social Forces articles.
原文の言語: 英語
Write article introductions and conclusions for sociology interview research. Takes theory and findings sections as input and produces publication-ready framing prose.
原文の言語: 英語
Build systematic literature databases for sociology research using OpenAlex API. Guides you through search, screening, snowballing, annotation, and synthesis with structured user interaction at each stage.
原文の言語: 英語
Deep reading and synthesis of literature corpus. Theoretical mapping, thematic clustering, and debate identification using Zotero MCP for full-text access.
原文の言語: 英語
Pragmatic qualitative analysis for interview data in sociology research. Guides you through systematic coding, interpretation, and synthesis with quality checkpoints. Supports theory-informed (Track A) or data-first (Track B) approaches.
原文の言語: 英語
Write-up support for qualitative interview research in sociology. Guides methods and findings drafting with emphasis on argument-driven narrative, not formulaic quote display.
原文の言語: 英語
Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.
原文の言語: 英語
Abductive analysis for qualitative interview data following Timmermans & Tavory. Guides you through theory-first analysis that recognizes anomalies and generates novel theoretical insights through systematic puzzle exploration.
原文の言語: 英語
Transform textbook chapters into engaging, evidence-based lectures with Google Slides. Guides instructors through learning outcomes, narrative design, active learning activities, and slide creation via Google Docs MCP.
原文の言語: 英語
R statistical analysis for publication-ready sociology research. Guides you through phased workflows for DiD, IV, matching, panel methods, and more. Use when doing quantitative analysis in R for academic papers.
原文の言語: 英語
Stata statistical analysis for publication-ready sociology research. Guides you through phased workflows for DiD, IV, matching, panel methods, and more. Use when doing quantitative analysis in Stata for academic papers.
原文の言語: 英語
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (LDA, STM) and neural (BERT, BERTopic) methods.
原文の言語: 英語