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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".