Getting to Open at a Closed Institution: A Case Study of Evolving and Sustaining Open Education Practices
Bibliographic record
Abstract
This study examined a Canadian post-secondary institution in the period between 2010 and 2014, with a follow-up assessment in 2018 in order to understand its evolution with open educational resources (OER) and open educational practices (OEP). In the first timeline, the study looked at drivers that contributed to the uptake of OER in relation to the type of OER and factors contributing to the diffusion of OER. In the second timeline, the study looked at whether OEPs are being sustained and how they evolved at the institution. Results show that within the institution there are both benefits and tensions to being open, and an institutional approach that considers openness on a case-by-case basis is appropriate. In looking at these two time periods, the study fills a gap in OER research by providing a more longitudinal view of an institutional shift towards initiating and sustaining openness.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".