Moving from EDID Words to Policy Action: A Case Study of a Teacher Education Program’s Admissions Policy Reform
Bibliographic record
Abstract
Regardless of the commitments that universities and teacher education programs (TEPs) have publicly stated regarding equity, diversity, inclusion, or decolonization (EDID), rarely do these commitments impact their admission policies or practices. Through examining a small program’s efforts at implementing EDID change over a three-year period, this article provides critical reflections, questions, and action steps for TEPs looking to move beyond talking about the importance of EDID, to actually altering policies and procedures to address systemic change. Utilizing the concepts of “equity in” and “equity through” admissions, intake variables (Multiple Mini Interview [MMI], Program Preparation, GPA) were analyzed quantitatively and used in this beginning participatory action research project. Results illustrate the benefits of the MMI, the need for program admissions to account for capacities in relation to anti-racism directly, rather than just generally referring to equity, and the need for admission practices to reflect an appreciation of the complexities around identity and ethics.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".