Two‐stage cluster samples with judgment post‐stratification
Bibliographic record
Abstract
Estimation of the population mean or total in a clustered population can be done using a two‐stage sampling design. Here we present design‐unbiased estimators and their variances and approximate confidence intervals for the population mean and total for sampling designs in which a cluster sampling is undertaken at each stage as either judgment post‐stratified (JPS) or simple random sampling (SRS). SRS is performed without replacement, while JPS designs can be implemented with or without replacement. The efficiency of JPS designs relative to the SRS design is investigated. The proposed estimators have smaller variances under JPS than the two‐stage SRS design. The gain in efficiency depends on the intra‐cluster correlation coefficient and the sampling design choices at each stage. To achieve a fixed cost, the optimal sample sizes are derived for each stage by maximizing the information content of the sample. The proposed sampling designs and estimators are illustrated with a real‐life agricultural sampling task for vineyard management.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".