A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Softer label and rationale representations outperform hard ones on predictive, distributional, plausibility, faithfulness, and complexity metrics when models are re-implemented across representation spaces in hate speech detection.
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From Ground Truth to Measurement: A Statistical Framework for Human Labeling
A statistical framework decomposes human annotation outcomes into four interpretable variation sources and extends classical measurement-error models to handle both shared and individualized notions of truth.
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Disagreeing Rationales: Rethinking Classification and Explainability Evaluation in Hate Speech Detection
Softer label and rationale representations outperform hard ones on predictive, distributional, plausibility, faithfulness, and complexity metrics when models are re-implemented across representation spaces in hate speech detection.