Toward more user-centric data access solutions: Producing synthetic data of high analytical value by data synthesis1
Bibliographic record
Abstract
Under the Modernization programme Statistics Canada has recently undertaken, the Agency is to put forward data access solutions that present greater analytical value to Canadians while maintaining its core values of protecting confidentiality of respondents’ information. One avenue currently explored is Data Synthesis as a means of delivering synthetic data with high analytical value to users. At the time of writing, Statistics Canada has publicly released synthetic versions of two different datasets related to census, mortality and cancer information. In both cases, synthetic data were generated using the R package synthpop. This paper describes the use of Data Synthesis as a proof of concept for modernizing Statistics Canada’s data access solutions.
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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 | 0.302 | 0.596 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".