Estimation Methods and Related Systems at Statistics Canada
Bibliographic record
Abstract
Summary This paper provides an overview of research in estimation techniques, their application, and the development of generalized estimation systems at Statistics Canada. In Canada, the demand for more detailed and better quality cross‐sectional data related to various sodo‐economic issues has increased significantly in recent years. Also, there has been increasing interest in longitudinal data to better understand and interpret the relationships among variables, necessitating the implementation of a number of large scale panel surveys by Statistics Canada. The paper briefly discusses estimation for longitudinal data and a weighting approach developed for cross‐sectional data Prom these surveys. For cross‐sectional household and business surveys, as well as the census of population, appropriate dibration estimators developed for each situation are briefly discussed. In addition, regression composite estimation, a method developed to improve the quality of cross‐sectional estimates from rotating panel surveys such as the Canadian Labour Force Survey, is presented. With regard to more detailed cross‐sectional estimates at sub‐provincial levels, different approaches to small area estimation developed for various programs are also presented. We SUmmarize the various modules developed lor the GeneraIiized Ektimation System. important new developments within the system include two‐phase estimation as well as the estimation of variance for a number of imputation procedures. We briefly review the status of current estimation research on selected topics as well as the direction of future research.
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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 | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.025 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.041 |
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".