Small Area Prediction of Proportions with Applications to the Canadian Labour Force Survey
Bibliographic record
Abstract
A small area procedure for a two-way table of proportions is developed, where the estimated proportions are from a complex survey. Estimation is difficult because the observed proportions do not have multinomial distributions, the observed proportions are correlated with estimated variances, benchmarking is required, and mean models are nonlinear. A predictor based on a nonlinear mixed model is specified for the proportions. No transformation of the observations is involved, and the estimation procedure gives predictions that are in the parameter space. A bootstrap estimator of the mean squared error of a benchmarked predictor is suggested and performed well in simulations. The procedure is applied to the proportions in the two-way table defined by occupations crossed with Canadian provinces. The direct estimators are from the Canadian Labour Force Survey (LFS), and the corresponding two-way table from the previous Canadian Census of Population provides auxiliary information. The application of the prediction procedure to the LFS data leads to gains in estimated mean squared errors relative to the direct estimators between approximately 30 percent and 80 percent. A comparison of the predictors to the Census 2006 proportions further supports the suggested procedures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".