Supporting an OER Finding Service: Utilizing Past Experiences
Bibliographic record
Abstract
Abstract for CAPAL 2025 Conference: Are you exploring ways to support educators at your postsecondary institution with finding and adopting Open Educational Resources (OER)? Have you wondered what skills or experiences can be helpful when supporting Open Education initiatives in your library? Course materials have seen a shift in recent years within academic libraries as publisher and student behavior has changed. At the same time, there has been growth of advocacy, awareness, and institutional support for the use and creation of Open Educational Resources (OER) within higher education. The use of OER can help to address textbook affordability issues experienced by students, support initiatives around equity, diversity, inclusion, and accessibility (EDIA), and be an alternative freely accessible and adaptable course material option for educators interested in developing their own curricular content. This presentation will highlight an OER finding service model, Open Course Material Matching Service (OCoMMS), based in Libraries and Cultural Resources (LCR) at the University of Calgary that reduces barriers and supports their teaching faculty and instructors discover potential OER for use in their courses or as the foundation for a new OER adaptation. Attendees will learn about what the Open Course Material Matching Service is, the workflows or steps taken following a request, and the staffing model used to support the service. Tied throughout will be strategies and connections to past experiences and knowledge that has been helpful in navigating the workflows of the OCoMMS service model and consideration of curricular needs and content. Attendees will come away with resources and strategies they may incorporate into their own OER finding service or adapt for library resource adoption services. Attendees will also have the opportunity to share their own practices and experiences that they have utilized for similar purposes in supporting educators with discovering library resources or OER to support learners.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 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 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".