Canadaâs New Open Access Policy: Integrating Libraries into Open Scholarship
Bibliographic record
Abstract
Canada’s new Open Access policy requires government-funded researchers — and encourages all Canadian researchers — to make their work publicly accessible by either publishing in Open Access journals or archiving it in repositories. This policy signals that Open Access is now a default setting for research in Canada and presents tremendous opportunities for libraries to support open scholarship through outreach, advocacy, support services and infrastructure. This presentation will explore policy, practice and implications for funders, institutions and researchers. We will focus on how libraries can facilitate the cultural shift to open research by raising awareness of the benefits of open scholarship, promoting institutional and subject repositories, and advising on copyright and intellectual property matters. We will share strategies for addressing concerns and/or barriers to Open Access with faculty and other key stakeholders, and discuss implications for libraries as partners in the scholarly communication process.
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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 | 0.034 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.035 | 0.019 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".