A Socio-Spatial Analysis of Communities Affected by Public School Closures in Ontario
Bibliographic record
Abstract
The prevalence of public school closures in Ontario is growing. Though schools provide \nextensive social benefits for communities they, the current Ministry of Education (MOE) model \nfor determining school closures called Pupil Accommodation Review Guidelines (PARG), \nprincipally relies on economic efficiency as criteria. In response to growing concern surrounding \nthe inequity of the current model – with apprehension that vulnerable communities are the \ndisproportionate targets —a moratorium on school closures was declared in June 2017 to \nrevamp the model. The proposed research aims to fill the existing gaps in data and research on \nOntario school closures to inform the creation of a model that minimizes hardship on \nvulnerable communities. Specifically, this research will produce a comprehensive and publicly- \navailable dataset of pending and completed school closure locations in Ontario since the \nestablishment of PARG in 2006 and a subsequent analysis that identifies socio-spatial inequities \nin Ontario school closures. This research will consist of four phases (school closure dataset \ncreation; acquisition of community socioeconomic profiles; data harmonization; and spatial \nanalysis) and will draw from Ontario public school board website archives for data creation and \nthe 2017 Ontario Marginalization Index (ON-Marg), for existing socioeconomic data. This \nresearch will make important contributions to research, policy, and practice in its production of \ndata and analysis that are presently non existent and its tremendous potential to influence \npolicy that can protect vulnerable communities from the permanent loss of public schools.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".