Bayesian sensitivity analyses for hidden sub‐populations in weighted sampling
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Bibliographic record
Abstract
Abstract In this paper, we propose several Bayesian model‐based approaches for sensitivity analyses on assessments of population averages and measures of association under complex models. In particular, the proposed methods adjust for a potential impact from a hidden sub‐population when weighted sampling from the non‐hidden sub‐population is possible. Bayesian models are presented for estimating population medical expenditure and health care utilization, as well as measures of association with a binary covariate. Large‐sample limiting versions of the posteriors are obtained for all the models. Using Medical Expenditure Panel Survey data, in which individuals with higher expenditures and more frequent health care visits are more likely to be included, we illustrate how the assumption about the hidden proportion of never‐respondents may impact the final estimates of expenditure, utilization, and measures of association with a binary covariate. The Canadian Journal of Statistics 42: 436–450; 2014 © 2014 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 | 0.001 | 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