Promoting a culture of openness: Institutional open access policy development and evaluation at a Canadian university
Bibliographic record
Abstract
Institutional open access (OA) policies can act as a solid foundation on which to build university-wide support for open access. This is the first paper to reflect on the entire process of developing, implementing, and reviewing an institutional open access policy at a Canadian post-secondary institution. Simon Fraser University (SFU) is one of a few Canadian universities with an institutional open access policy. As a leader in open access, SFU is well positioned to share observations of our experiences in the first three years of our OA policy. Throughout this paper, we reflect on the role that the policy plays in the broader culture of openness at SFU and on the OA resources and supports provided to SFU researchers. Other institutions may find our observations and adoption of the SOAR (strengths, opportunities, aspirations, results) appreciative inquiry framework useful as they explore future policy development or review and work to promote a culture of open access within their university community.
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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 | 0.188 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.036 | 0.014 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".