Clarifying Some Aspects of Variance Estimation in Two-Phase Sampling
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of variance estimation in two-phase sampling designs. The usual variance estimators suffer from two drawbacks: their computation requires specialized software designed for two-phase sampling, and they depend on the second-phase joint inclusion probabilities, which may be difficult to obtain. We consider a simplified variance estimator and study its properties with respect to several two-phase designs. The extension to calibration estimators is considered. We establish interesting links between the proposed simplified variance estimator and resampling variance estimators studied in Kott and Stukel (1997) and Kim, Navarro, and Fuller (2006). In particular, we shed new light on a long-standing issue that has been raised in Kott and Stukel (1997). These authors showed that the jackknife method leads to a consistent variance estimator of the reweighted estimator but to an inconsistent variance estimator of the double expansion estimator. We give a simple explanation why this is so.
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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 | 0.021 | 0.035 |
| 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