Distributionally Conservative Stochastic Dominance via Subsampling
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Bibliographic record
Abstract
ABSTRACT This note defines distributionally conservative versions of stochastic dominance relations based on subsampling. It presents a non‐asymptotic analysis of the probability of the false dominance (FD) error for the empirical version of the subsampling‐based empirical dominance procedure. The analysis is based on the generalization of the McDiarmid's concentration inequality to ‐mixing processes by Kontorovich and Ramanan. The concentration bounds obtained depend on the entropy characteristics of the problem, such as the Lipschitz coefficients of the utilities involved, the FD parameters involved, and the coefficients that represent temporal dependence at each subsample. The analysis establishes tighter concentration bounds for the conservative procedure in both stationary and nonstationary cases when the subsampling rate is appropriately chosen.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it