Excessive rightsizing? The interdependence of public school closures and population shrinkage
Bibliographic record
Abstract
Abstract Shrinking cities have, by definition, lost population. Rightsizing is a strategic planning approach to mitigate the challenges of population loss by adjusting a municipality's services, amenities, or even footprint to fit a new demographic reality. While studies have documented the unacceptability and ineptitude of municipality‐driven rightsizing, public school closures have proliferated and quietly become a noteworthy material manifestation of population change. However, as public schools are widely considered to be a foundational component of community cohesion, identity, and prosperity, it begs the question of whether their closure may accelerate the decline feedback mechanisms already present in many shrinking cities. Our study examines public school closures in Ontario, Canada, from 2011 to 2016 to determine the relationship between municipal population trajectories and size and public school closures, and to explore the prevalence of school closures and the community context in shrinking Ontario municipalities. We find that public school closures occurred disproportionately in shrinking and smaller municipalities. Furthermore, public school closure prevalence is associated with low income, low ethnoracial diversity, and low educational attainment .
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".