Reimagining Leadership in Open Education: Networking to Promote Social Justice and Systemic Change
Bibliographic record
Abstract
Challenging traditional notions of leadership, and leveraging non-hierarchical learning structures, the Regional Leaders of Open Education Network (RLOE) was created to bring together leaders from a broad diversity of institutions in the U.S. and Canada to build strategic plans for open education that especially support underserved and underrepresented students. All members of the network, including an advisory team, collaborators, student mentors and cohort participants were engaged in a multi-directional learning program over two years (2021-2022) that included a variety of synchronous and asynchronous online engagement opportunities, as well as the opportunity to attend an in-person summit. Analyses of surveys and reports completed by network participants indicated that RLOE was successful in building community and in providing vital networking opportunities that supported them to design and begin to implement open education strategic plans that included initiatives in professional development, forming partnerships, integrating DEI as well as many other goals and accomplishments. Cohort participants indicated statistically significant gains in 1) developing and leveraging their leadership skills to serve marginalized and underrepresented students, 2) understanding how OE practices can empower all students, especially marginalized students, and 3) how OER can be used to specifically support underrepresented and underserved groups. In addition, 90% of cohort participants indicated that the RLOE Network helped them to center principles of diversity, equity and inclusion into their open educational work.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".