Controlled Access Management for Research Data Initiative (CAM) Report & Recommendations
Bibliographic record
Abstract
Canadian researchers are increasingly expected to share their data and make them available for re-use. However, not all data can be shared openly: privacy and legal issues, intellectual property and commercial obligations, and ethics considerations are just a few reasons that may necessitate access restrictions to a dataset. As a result, the Canadian research ecosystem faces a demand for strategies and support for managing research data requiring controlled access. The Controlled Access Management for Research Data (CAM) Initiative is a national initiative led by the Digital Research Alliance of Canada (the Alliance) in collaboration with 29 Canadian institutions and organizations, or Partner Organizations (POs). The goal of the initiative is to advance the Canadian research ecosystem’s capacity to support the management of controlled-access research data. Over the course of a year, Partner Organizations and their staff worked collaboratively to map the current landscape of controlled access management in Canada; identify the needs and challenges facing Canadian institutions that support controlled access management; and develop recommendations to advance Canada’s capacity to support controlled access management of research data.
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 | 0.068 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".