Replication variance estimation in unequal probability sampling without replacement: One‐stage and two‐stage
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Replication‐based variance estimation methods including the bootstrap, balanced repeated replication, and the Jackknife have been studied extensively. They have been applicable primarily to stratified multistage sampling designs in which the clusters within strata are sampled with replacement or the first‐stage sampling fraction is negligible with a notable exception of a two‐stage cluster sampling with equal probability and without replacement in Rao & Wu (1988). It is common practice, however, that the first‐stage sampling fraction may not be negligible, resulting in overestimation. To alleviate this practical issue, we derive the balanced repeated replication methods and the bootstrap methods for one‐ and two‐stage stratified unequal probability sampling, where the sampling fractions are not negligible. The asymptotic property of the proposed methods is studied. In addition, the methodologies are applied to a simulated population with characteristics of a real sample survey. The Canadian Journal of Statistics 41: 696‐716; 2013 © 2013 Statistical Society of Canada
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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 | 0.002 | 0.005 |
| 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