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.
Political Analysis , year =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
Adaptive Matrix Validation calibrates AI-mapped survey responses using sparse randomized validation questions from other respondents then corrects with the target's own answers, with estimators and planning formulas for means, subgroups, and regressions.
Zero-shot TSFMs conditioned on leakage-safe covariates from Google Trends and an institutional index forecast commencing enrolments competitively with classical methods under data sparsity.
citing papers explorer
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Measuring Judgment Quality in Natural-Language Explanations: Evidence from Forecasting Tournaments
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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Correct codes for the wrong reasons? validating LLMs as measurement instruments for theoretical constructs
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.
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When Surveys Become Conversations: Adaptive Matrix Validation for AI-Assisted Interviews
Adaptive Matrix Validation calibrates AI-mapped survey responses using sparse randomized validation questions from other respondents then corrects with the target's own answers, with estimators and planning formulas for means, subgroups, and regressions.
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Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning
Zero-shot TSFMs conditioned on leakage-safe covariates from Google Trends and an institutional index forecast commencing enrolments competitively with classical methods under data sparsity.