Statistical Population Register: using administrative in the Canadian Census
Bibliographic record
Abstract
IntroductionFor a number of years, Statistics Canada has been evaluating the potential of increasing its use of administrative data into its Census Program. The research conducted so far has revealed that it could be possible to produce the census population counts by using existing administrative data. Objectives and ApproachThe building of the Statistical Population Register (SPR) is one step towards achieving the use of administrative data into the Canadian Census Program. In addition, the SPR, in combination with the Business Register and the Statistical Building Register, would support a more efficient production of statistics via a multi-register-based system in the future. The SPR is created by linking numerous administrative data sources (federal, provincial, municipal and private). The in-scope Canadian population is then identified and extracted from the SPR. ResultsThe presentation will focus on the reasons as well as the goals that had to be met in the initial research project in order to demonstrate the potential of using administrative data within the Census Program. The current state of the project will be highlighted by presenting high-level results at the Canadian, provincial and territorial levels. This is accomplished by comparing the Statistical Population Register’s in-scope population to its reference, Statistics Canada’s official population counts. Conclusion/ImplicationsDespite promising results, areas of improvements have already been identified and work is under way in order to improve the quality of the upcoming Statistical Population Register. The final section of the presentation will be devoted to the future research agenda of the Census Program.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.037 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".