Bootstrapped inference for variance parameters, measures of heterogeneity and random effects in multilevel logistic regression models
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Bibliographic record
Abstract
We used Monte Carlo simulations to assess the performance of three bootstrap procedures for use with multilevel data (the parametric bootstrap, the residuals bootstrap, and the nonparametric bootstrap) for estimating the sampling variation of three measures of cluster variation and heterogeneity when using a multilevel logistic regression model: the variance of the distribution of the random effects, the variance partition coefficient (equivalent here to the intraclass correlation coefficient), and the median odds ratio. We also described a novel parametric bootstrap procedure to estimate the standard errors of the predicted cluster-specific random effects. Our results suggest that the parametric and residuals bootstrap should, in general, be used to estimate the sampling variation of key measures of cluster variation and heterogeneity. The performance of the novel parametric bootstrap procedure for estimating the standard errors of predicted cluster-specific random effects tended to exceed that of the model-based estimates.
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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 | 0.008 |
| 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