A simulation study of sampling in difficult settings: Statistical superiority of a little-used method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Taking a representative sample to determine prevalence of variables such as disease or vaccination in a population presents challenges, especially when little is known about the population. Several methods have been proposed for second stage cluster sampling. They include random sampling in small areas (the approach used in several international surveys), random walks within a specified geographic area, and using a grid superimposed on a map. We constructed 50 virtual populations with varying characteristics, such as overall prevalence of disease and variability of population density across towns. Each population comprised about a million people spread over 300 towns. We applied ten sampling methods to each. In 1,000 simulations, with different sample sizes per cluster, we estimated the prevalence of disease and the relative risk of disease given an exposure and calculated the Root Mean Squared Error (RMSE) of these estimates. We compared the sampling methods using the RMSEs. In our simulations a grid method was the best statistically in the great majority of circumstances. It showed less susceptibility to clustering effects, likely because it sampled over a much wider area than the other methods. We discuss the findings in relation to practical sampling issues.
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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 | 0.007 |
| 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