Variance estimation for two-phase stratified sampling
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Bibliographic record
Abstract
The authors consider variance estimation for the generalized regression estimator in a two-phase context when the first-phase sample has been restratified using information gathered from the first-phase sample. Simple computational expressions for variance estimation are provided for the double expansion estimator and the reweighted expansion estimator of Kott & Stukel (1997). These estimators are compared using data from the Canadian Retail Commodity Survey. RÉSUMÉ Les auteurs s'intéressent à l'estimation de la variance de l'estimateur de régression généralisé pour un plan de sondage à deux phases dans le cas où l'échantillon de première phase a été stratifié à partir d'information auxiliaire disponible pour cette phase. Des expressions simples sont fournies pour l'estimation de la variance de l'estimateur doublement dilaté et de l'estimateur repondéré de Kott & Stukel (1997). Ces estimations sont companées au moyen de données provenant de l'Enquěte canadienne sur les marchandises de détail
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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 | 0.002 |
| 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