A comparative study of robust tests for spread: Asymmetric trimming strategies
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Bibliographic record
Abstract
We examined 633 procedures that can be used to compare the variability of scores across independent groups. The procedures, except for one, were modifications of the procedures suggested by Levene (1960) and O'Brien (1981). We modified their procedures by substituting robust measures of the typical score and variability, rather than relying on classical estimators. The robust measures that we utilized were either based on a priori or empirically determined symmetric or asymmetric trimming strategies. The Levene-type and O'Brien-type transformed scores were used with either the ANOVA F test, a robust test due to Lee and Fung (1985), or the Welch (1951) test. Based on four measures of robustness, we recommend a Levene-type transformation based upon empirically determined 20% asymmetric trimmed means, involving a particular adaptive estimator, where the transformed scores are then used with the ANOVA F test.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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