Integration of Nonprobability and Probability Samples via Survey Weights
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
Probability sample encounters the problems of increasing cost and nonresponse. The cost has rapidly been increasing in executing a large probability sample survey, and, for some surveys, response rate can be below the 10 percent level. Therefore, statisticians seek some alternative methods. One of them is to use a large nonprobability sample (S_1 ) supplemented by a small probability sample (S_2 ). Both samples are taken from the same population and they include common covariates, and a third sample (S_3 ) is created by combining these two samples; S_1  can be biased and S_2  may have large sample variance. These two problems are reduced by survey weights and combining the two samples. Although S_2  is a small sample, it provides good properties of unbiasedness in estimation and of survey weights. With these known weights, we obtain adjusted sample weights (ASW), and create a sample model from a finite population model. We fit the sample model to obtain its parameters and generate values from the population model. Similarly, we repeat these processes for other two samples, S_1  and S_3  and for different statistical methods. We show reduced biases of the finite population means and reduced variances.as the combined sample size becomes large. We analyze sample data to show the reduction of these two errors.
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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 | 0.003 | 0.015 |
| 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