Why this work is in the frame
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Bibliographic record
Abstract
Yohai and Zamar's τ-estimators of regression have excellent statistical properties but are nevertheless rarely used in practice because of a lack of available software and the general impression that τ-estimators are difficult to approximate. We will show, however, that the computational difficulties of approximating τ-estimators are similar in nature to those of the more popular S-estimators. The main goal of this article is to compare an approximating algorithm for τ-estimators based on random resampling with some alternative heuristic search algorithms. We show that the former is not only simpler, but that when enhanced by local improvement steps it generally outperforms the consider edheuristic search algorithms, even when the seheuristic algorithms also incorporate local improvement steps. Additionally, we show that the random resampling algorithm for approximating τ-estimators has favorable statistical properties compared to the analogous and widely used algorithms for S- and least trimmed squares estimators.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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