A roadmap for responsible restricted data sharing in Canada: A panel presentation at the Canadian Conference on Open Science and Open Scholarship 2025
Bibliographic record
Abstract
This presentation uses current research efforts to highlight the challenges and gaps associated with finding and accessing restricted data in Canada. Topics include reporting on data availability in publications, creating open metadata for access requirements, enhancing repository infrastructure to support restricted data, and developing data sharing training programs for research communities who work with restricted data. From these discussions, the presentation outlines new research initiatives that explore these areas and provides a roadmap for how the Tri-agency, research institutions, librarians, and research communities can work towards a Canadian data sharing ecosystem that supports findable and accessible restricted data.
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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 | 0.069 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.038 | 0.013 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".