Intention, Tenacity, and a Bit of Serendipity: How We’ve Grown Open on the Canadian Prairies
Bibliographic record
Abstract
The University of Saskatchewan has seen the use of OER grow from the first large adoption of an open textbook in 2015 to a large-scale open initiative sweeping the institution. Some of our successes have included saving students $800,000 within 4 years, the creation and adaptation of several open textbooks, the piloting of an OER repository for storing and sharing locally created resources, and the integration of open pedagogy in at least half a dozen courses. As this initiative continues to grow, and brings about a culture shift within the institution, librarians play a vital role in educating and supporting both instructors and students about all aspects of open learning. This session will explore how an OER initiative has grown at the University of Saskatchewan, and how librarians at your institution can move such programs forward. We’ll share high level ideas and examples of how librarians and educational developers have worked at the grass roots level to seek out opportunities, educate others about OER, build partnerships, support champions, and grow the interest level and integration of OER at the University.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.068 | 0.051 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".