Robust model‐based stratification sampling designs
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
Abstract We address the resistance, somewhat pervasive within the sampling community, to model‐based methods. We do this by introducing notions of “approximate models” and then deriving sampling methods which are robust to model misspecification within neighbourhoods of the sampler's approximate, working model. Specifically we study robust sampling designs for model‐based stratification, when the assumed distribution of an auxiliary variable x , and the mean function and the variance function in the associated regression model, are only approximately specified. We adopt an approach of “minimax robustness,” to which end we introduce neighbourhoods of the “working” , and working regression model, and maximize the prediction mean squared error ( MSE ) for the empirical best predictor, of a population total, over these neighbourhoods. Then we obtain robust sampling designs, which minimize an upper bound of the maximum MSE through a modified genetic algorithm with “artificial implantation.” The techniques are illustrated in a case study of Australian sugar farms, where the goal is the prediction of total crop size, stratified by farm size. The Canadian Journal of Statistics 43: 554–577; 2015 © 2015 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 | 0.006 |
| 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 | 0.001 | 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