Trials & Tribulations of OER in the Institutional Repository: A Canadian Perspective
Bibliographic record
Abstract
Open Educational Resources (OER) are free teaching and learning resources that use an open licence. This allows instructors to freely reuse, adapt, and remix the resources to meet their specific teaching and learning context. As OER have continued to grow in popularity as course learning materials, faculty at post-secondary institutions have begun to create OER adaptations, as well as new OER. While this proliferation of free, openly licensed learning materials has been a boon for students and faculty and support UN SDG 4: Quality Education, it has also created a challenge in terms of the preservation and dissemination of OER. This presentation highlights the difficulties that York University, a Canadian post-secondary institution, faced when creating an OER collection within their open access institutional repository (IR). The presenters will cover some of the advantages of using a DSpace instance, while also highlighting challenges such as file types and version control for OER. Additionally, the presentation will highlight other examples of preservation and dissemination of OER in the Canadian post-secondary landscape. Ultimately, this presentation will explore key considerations repository managers should examine when making the decision to add OER to their IR or if a separate repository solution is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".