Methods used for Small Domain Estimation of Census Net Undercoverage in the 2001 Canadian Census
Bibliographic record
Abstract
Since the 1991 the population estimates have used the Census counts adjusted for the net Census undercoverage. The population estimates require the net missed persons by single year of age for each sex for all provinces and territories are required. While the coverage studies can provide reliable estimates of missed persons for each province and territory, the sample size is not large enough to provide reliable estimates at the detail needed. A mixture of procedures is used to produce the estimates. Direct survey estimates for each province and territory are used to create one margin. A spline smoothing method is used to produce the national estimates of age and sex needed for the other margin. An Empirical Bayes regression model creates the estimates for broad age groups within a province. A synthetic model then generates the detailed single year of age estimates. Finally, a calibration procedure is used to ensure the detailed estimates are consistent with the fixed marginal totals. Of some concern is the measure of the quality of these estimates. A Mean Square Error (MSE) is produced for the Empirical Bayes regression model but this does not take into account all the adjustments made to the model. This paper will review and compare procedures for estimating the MSE for this small domain estimation problem.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".