EQMs, sixty LLM-scored reasoning patterns, predict forecast accuracy at both item and person levels and outperform prior text-analysis methods in a large pre-registered tournament dataset.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Grain calibration decomposes theoretical constructs into clause-level components, tests each with extractive evidence, and combines results through explicit theory-derived rules to validate LLM coding beyond agreement with human annotators.
Semantic mapping of 8,954 definitions and 2,700 scales from 14,000+ papers shows learner agency and autonomy span task regulation, personal motivation, and sociocultural dimensions, with existing scales and generative AI research underrepresenting the sociocultural dimension.
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Large-scale semantic mapping of learner agency and autonomy reveals what measurement and generative AI research overlook
Semantic mapping of 8,954 definitions and 2,700 scales from 14,000+ papers shows learner agency and autonomy span task regulation, personal motivation, and sociocultural dimensions, with existing scales and generative AI research underrepresenting the sociocultural dimension.