Weighting in the regression analysis of survey data with a cross‐national application
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A class of survey weighting methods provides consistent estimation of regression coefficients under unequal probability sampling. The minimization of the variance of the estimated coefficients within this class is considered. A series of approximations leads to a simple modification of the usual design weight. One type of application where unequal probabilities of selection arise is in cross‐national comparative surveys. In this case, our argument suggests the use of a certain kind of within‐country weight. We investigate this idea in an application to data from the European Social Survey, where we fit a logistic regression model with vote in an election as the dependent variable and with various variables of political science interest included as explanatory variables. We show that the use of the modified weights leads to a considerable reduction in standard errors compared to design weighting. The Canadian Journal of Statistics 40: 697–711; 2012 © 2012 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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