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  "Title": "Qualitative Analysis with Large Language Models",
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  "Description": "Tools for AI-assisted qualitative data coding using large\nlanguage models ('LLMs') via the 'ellmer' package, supporting\nproviders including 'OpenAI', 'Anthropic', 'Google', 'Azure',\nand local models via 'Ollama'. Provides a 'codebook'-based\nworkflow for defining coding instructions and applying them to\ntexts, images, and other data. Includes built-in 'codebooks'\nfor common applications such as sentiment analysis and policy\ncoding, and functions for creating custom 'codebooks' for\nspecific research questions. Supports systematic replication\nacross models and settings, computing inter-coder reliability\nstatistics including Krippendorff's alpha (Krippendorff 2019,\n<doi:10.4135/9781071878781>) and Fleiss' kappa (Fleiss 1971,\n<doi:10.1037/h0031619>), as well as gold-standard validation\nmetrics including accuracy, precision, recall, and F1 scores\nfollowing Sokolova and Lapalme (2009,\n<doi:10.1016/j.ipm.2009.03.002>). Provides audit trail\nfunctionality for documenting coding workflows following\nLincoln and Guba's (1985, ISBN:0803924313) framework for\nestablishing trustworthiness in qualitative research.",
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