The Toronto District School Board: A global city school system's structures, processes, and student outcomes
Bibliographic record
Abstract
In this article we describe the ways that academic opportunity is distributed within the Toronto District School Board (TDSB), Canada’s largest and most demographically diverse public education system. By putting a range of recent outcome data into historical, organizational, and policy contexts, we provide a snapshot of how one of North America's largest school systems works in ways that simultaneously reinforce, and challenge, patterns of academic stratification. Although schooling in some global cities is shaped by decentralization, competition, and a 'school reform industry', public education in Toronto is very much characterized by centralization and increased public investment. Therefore, this paper queries whether these larger historical and structural factors lead to greater equity for racialized and minoritized communities. Through the infusion of equity-focused policies and anti-discrimination-centred interventions, can the case be made that marginalized groups are navigating the school system with greater success? Reviewing historical and recent data from the Toronto Board of Education and TDSB, we reflect on and query the extent of disparity that continues to exist, problematizing the disconnect between policy and addressing the root causes of inequality.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".