Converting percentage scores to A/B/C/D grades reduces information entropy by 69 percent, makes optimal student clusters sensitive to single data points, and drops temporal diagnostic consistency from 93-96 percent to 52-96 percent.
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2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Human tests should not be applied to AI to measure traits like intelligence due to calibration, validity, contamination, and prompt sensitivity issues; develop AI-specific evaluation frameworks instead.
citing papers explorer
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Data Aphasia: An Institutional Counterfactual Study of the Stability of Academic Cognition Under Letter-Grade Evaluation Systems
Converting percentage scores to A/B/C/D grades reduces information entropy by 69 percent, makes optimal student clusters sensitive to single data points, and drops temporal diagnostic consistency from 93-96 percent to 52-96 percent.
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Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead
Human tests should not be applied to AI to measure traits like intelligence due to calibration, validity, contamination, and prompt sensitivity issues; develop AI-specific evaluation frameworks instead.