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.
Xue, Jackson Trager, Peter S
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Prompting LLMs to report variability and use clear scenarios improves alignment with human moral judgment distributions on two datasets.
LLMs accelerate research workflows from idea generation to writing but introduce challenges like hallucination, bias, opacity, and ten systemic risks requiring new governance frameworks.
citing papers explorer
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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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LLMs Can Better Capture Human Judgments--With the Right Prompts
Prompting LLMs to report variability and use clear scenarios improves alignment with human moral judgment distributions on two datasets.