The Canadian experience of building a privacy-responsible integrated statistical register infrastructure
Bibliographic record
Abstract
Statistics Canada has maintained statistical business and address registers for decades. Its Statistical Business Register is continuously being modernized to adapt to needs for more and more timely business and institutional statistics at lower levels of geography. The statistical address register is being replaced in 2022 by a Statistical Building Register that expands coverage to the non-residential building units and includes more attributes. Since 2016, Statistics Canada had also been investigating options to add a population component to this integrated system of registers. The organization settled in 2021 on a privacy-responsible population linkage infrastructure that is designed with privacy in mind from the onset. This paper presents how the privacy landscape has evolved and shaped the statistical register infrastructure in Canada. It also describes the Secure Infrastructure for Data Integration that will be elaborated to produce reference population files to support the production of statistical information for Canadians, and how it is entrenched in privacy principles.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".