The Federal Open Science Repository of Canada: A Key Destination on Canada's Roadmap to Open Science
Bibliographic record
Abstract
Until now, many Government of Canada scientists and researchers have not had the infrastructure to make their scientific publications openly available. This gap has been addressed by the Federal Open Science Repository of Canada (FOSRC), a shared repository that launched January 2024 to make federally funded scientific outputs accessible to all. The FOSRC, which will help meet key recommendations of Canada's Roadmap for Open Science, is a horizontal initiative among federal departments. Collaborators include the Office of Chief Science Advisor of Canada, Shared Service Canada, the Federal Science Libraries Network/National Research Council Canada, and eight science-based departments and agencies.The primary goal of this repository is to deliver a practical solution for a policy-driven recommendation to provide transparency and open access to Canadian research. The approach to this large-scale project was to establish a collaboration model for governance, operations, and technical development. Within this model, a business owner was established to work through governance committees for decision-making; oversee financial and operations management; liaise on product development; and ensure success for the strategic vision. Undertaking a shared repository project with varied interests is challenging and rewarding, requiring clear strategic direction, financial support, flexibility, and close collaboration.
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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 | 0.053 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.029 | 0.014 |
| Scholarly communication | 0.044 | 0.018 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".