Advancing Open at Simon Fraser University: the Faculty and Student Experience
Bibliographic record
Abstract
Over the past three years, open advocates at Simon Fraser University have been successful in advancing the open agenda on campus. As an early career faculty member, Dr. Juan Pablo Alperin has already played a leading role in openness at SFU. Dr. Alperin will share his work in advancing scholarly communications at SFU which include passing Canada’s second institutional open access policy and changing the tenure and promotion requirements within his department to include openness. Brady Yano is a former student leader from Simon Fraser University. During his two year's spent on the board of the Simon Fraser Student Society, Brady was a vocal advocate for open educational resources (OER), and led a successful campaign called #textbookbrokeBC. His presentation will provide an overview of his journey as a young open advocate and will explore some of the challenges and opportunities of influencing institutional culture as an undergraduate student.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.012 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".