Putting Equity into Practice using Open Education: Strategic and Practical Approaches
Bibliographic record
Abstract
Equity work and open educational resources may seem like an obvious pairing, but how much do we as open education advocates connect these dots and articulate this important relationship? The University of Northern Colorado (UNC) was one of five U.S. institutions to receive a grant to pilot the Driving OER Sustainability for Student Success (DOERS3) Equity Through OER Rubric. Members (36 higher education systems in the U.S. and Canada) of 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 this rubric can be utilized to assess institutional equity efforts and capacity for imbedding Equity Through OER into the operations of an institution or system. Attendees will be invited to reflect on 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 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 | 0.069 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.096 |
| Scholarly communication | 0.039 | 0.049 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".