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Align Before You Combine: Reference Space Calibration for Supervision Without Ground Truth
We introduce a calibration-first framework that produces supervision scores without access to ground-truth labels or a shared annotation space. Our framework aligns subset-specific scorers using a synthetic ordinal reference space before fusion. This reference space is constructed from ordered calib
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