1 Enabling Dynamic Linkage of Linguistic Census Data at Statistics Canada
Bibliographic record
Abstract
Abstract—We explore the applicability of two privacypreserving mechanisms to solve a real-world problem of strategical importance in population health research in Canada. The goal is to enable dynamic and automated linkage between health data (hosted at ICES – a provincial agency) and language variables from the 2006 census (hosted at Statistics Canada – a federal agency). The first mechanism relies on a pattern of interaction that, by design, prevents the collection of residual information, all the while making assumptions on the entities ’ intentions. The second makes no such assumption and relies instead on the true understanding of what statistical leakage is at play, thanks to recent advances in private data analysis (a field at the confluence of cryptology, statistics, and database systems). In particular, we show how results from this domain make it possible to characterize the worst-case information leakage, and support the argument that data utility remains achievable for virtually any level of risk acceptable. This demonstrates a form of linkage is possible between health data and linguistic data in Canada, despite the seemingly hard obstacles induced by its multi-level privacy policies. Based on these observations, we suggest some directions to implement the proposed mechanisms, whose availability would create unprecedented opportunities for population health researchers. A. The context I.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".