Socio-spatial dimensions of school closures and neighbourhood change in Ontario: An environmental injustice?
Bibliographic record
Abstract
In addition to their educational purposes, public schools and their surrounding properties are essential to community liveability, as they enrich the daily lives of children, parents, and nearby residents. Yet, decisions are being made to close schools in Ontario, Canada based on declining enrolments, without due consideration of these benefits. Since 2011, over 400 public schools have been closed in Ontario, causing communities across the province to lose essential hubs. In a province where significant socio-spatial inequities persist, public school closures could worsen the conditions of daily living for residents in neighbourhoods that have already been deprived of resources and opportunities through failed public policy. The objectives of this study were to document the spatial scope of public school closures in Ontario, to understand the population change profiles in communities where closures happened, and to elucidate how these closures temporally relate to structural vulnerabilities of the communities in which these closures took place. Using Census-derived deprivation index scores geo-coded dataset to both currently open and recently closed public schools in Ontario, our analysis revealed three key findings. First, school closures have occurred disproportionately in small to mid-sized cities and rural communities. Second, there is no evidence of significantly declining child populations prior to school closures, in communities where schools closed. And third, closures were more common in higher deprivation communities in small to mid-sized cities. Taken together, these findings offer critical insights on the challenges that many communities face due to insufficient and inequitable policies that govern school closure decisions in Ontario. The study signals an urgent need for a more collaborative, forward-thinking, and equity-oriented school closure decision-making model that supports residents and protects communities from losing a vital public asset.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".