Statistical Population Register: using administrative in the Canadian Census
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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