Enabling dynamic linkage of linguistic census data at Statistics Canada (extended abstract)
Bibliographic record
Abstract
Research in population health consists in studying the impact of various factors (determinants) on health, with the longterm objective of yielding better policies, programs, and services. Researchers of Official Language Minority Communities (OLMCs) focus specifically on determinants related to speaking a minority language, such as English in Quebec, or French in the rest of Canada. Investigations of this type require the possibility of associating health data to linguistic information. Unfortunately, the largest health databases in Ontario, held at the Institute for Clinical Evaluative Sciences (ICES), do not contain usable linguistic variables to date. High-quality language variables however exist at Statistics Canada (2006 Census), and we are interested in enabling its linkage to ICES health data in a dynamic way. The linkage we consider is intrinsically transient and aggregated: it consists in allowing ICES to learn interactively how many Francophones are present in a given sample of individuals (sum queries). We suggest two possible privacy-preserving mechanisms to enable dynamic sum queries: 1) by constraining the dataflow itself; 2) by adapting recent results ([1]) to characterize what leakage is at play in our scenario and what parameters impact the tradeoff between leakage and utility. We rely on these results to argue that a safe exposition of linguistic data could indeed be envisioned, and beyond, that similar techniques could be used to enrich provincial health databases in general with a range of federal census data, making it possible to perform fine-grained community-based studies in Canada.
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.
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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".