A New Initiative: Access to the Statistics Canada's Public Use Microdata Files Collection
Bibliographic record
Abstract
For many years, users were indicating the difficulties in accessing the Public Use Microdata Files from Canada in order to conduct international comparisons or to conduct studies on the Canadian society.nbsp; In order to respond to that particular need to access the full Statistics Canada's Public Use Microdata collection, a subscription fee service has been put in place. This service aims at national and international organisations that are not members of the Canadian Data Liberation Initiative who would like to use and share statistics Canada's Public Use Microdata Files within their organisations for non-commercial purposes. This service offers access as well as support through a listserv.nbsp; We hope that through this service which offers a one stop shop, users will be able to add the Canadian perspective to their studies.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.040 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.039 |
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".