Tensions and Transformations: Equity, Diversity, and Inclusion in a Diverse Urban School Division
Bibliographic record
Abstract
This study examines how equity, diversity, and inclusion (EDI) are understood and implemented within a large, diverse, urban school division in Canada. Using a mixed-methods approach and grounded in theoretical perspectives, including critical race theory, intersectionality, the ecology of inclusive education, and collective efficacy theory, we gathered insights from surveys and focus groups with educators, administrators, clinicians, educational assistants, caregivers, and students. Findings suggest that, while support for EDI is broad, the implementation is inconsistent and shaped by systemic barriers, resource constraints, and unclear leadership. We highlight the need for shared language, coordinated supports, and ongoing professional learning and instructional redesign. We offer a model for examining EDI at a systems level and share lessons to guide sustained transformation in other school organizations.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.027 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".