A Journey in OER: Growing Awareness, Discovery, and Use of OER at the University of Calgary
Bibliographic record
Abstract
Conference Presentation at the Open Education Conference 2022. At this session attendees will learn about the University of Calgary's Libraries and Cultural Resources' journey in open educational resources. We'll share our recent initiatives and strategies for expanding capacity in our journey to building a community around OER on campus. More specifically we'll discuss expanding roles within the library to support OER, OER discovery initiatives, awareness and learning opportunities, grant funding, and the development of a new Open Course Materials Matching Service (OCoMMS). Attendees will gain insight into UCalgary's journey and practices in the early stages of OER advocacy. By attending this session, attendees will be able to: Understand UCalgary Libraries and Cultural Resources' journey in Open Educational Resource advocacy; Consider how to apply UCalgary's OER initiatives and resources within their own institution; Identify ways to engage with and support campus partners and other OER advocates.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.005 |
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".