PROVIDING GREATER ACCESSIBILITY TO SURVEY DATA FOR ANALYSIS
Bibliographic record
Abstract
This paper will discuss three approaches developed by the Agency to provide researchers with access to data produced by complex surveys: public use microdata files, remote access and research data centres. II. PUBLIC USE MICRODATA FILES (PUMF) 2. Statistics Canada began producing public use microdata files following the 1971 revision to the Statistics Act that made possible the public release of non-confidential microdata. The release of a microdata file for a survey is authorised by the Agency only when doing so substantially enhances the analytical value of the data. Planned microdata products are submitted to a Microdata Release Committee which must be satisfied that all reasonable efforts have been made to protect the identity of respondents before it grants permission for the release of a public use microdata file. Submissions to the committee must include documentation of the survey and its data contents as well as a description of measures taken for disclosure protection. The onus is on the survey manager to take all the necessary steps to ensure that the microdata can be released and to convince the Committee that all possible measures have been taken to protect the confidentiality of survey respondents. Microdata can only be released for sample data. Since 1971, a total of 371 public use files have been reviewed and 345 have been approved by the Microdata Release Committee for public dissemination. 3. In recent years, changes in the nature and uses of survey data have led to additional considerations with regards to PUMF disclosure protection. These have to do with linkages with external files, the estimation of variances and the protection of longitudinal survey microdata. These will be treated in turn
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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 | 0.163 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.128 | 0.031 |
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".