Driving OER Sustainability for Student Success: Assessing and Acting on the Equity Dimensions of OER
Bibliographic record
Abstract
Equity work and open educational resources may seem like an obvious pairing, but how much do we as OER advocates connect these dots and articulate this relationship? The University of Northern Colorado (UNC) was one of five U.S. institutions to receive a grant to pilot the Equity Through OER Rubric. Members (36 higher education systems in the U.S. and Canada) of the Driving OER Sustainability for Student Success (DOERS3) created the rubric to help define, unpack, and explain the multiple dimensions of equity and foreground the role of OER in closing equity gaps. The final rubric project portfolio includes a theoretical framework, the rubric, and case studies of institutions/systems that have utilized the rubric. Attendees will learn how the rubric can be utilized to assess institutional equity efforts and capacity for embedding Equity Through OER into the operations of an institution/system. Attendees will be invited to reflect and share their ideas and approaches regarding connecting equity and OER at their institutions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".