Reacting to Change: Exploring the Potential Impacts of School Closures on Households and Neighbourhoods in Kingston, Ontario
Bibliographic record
Abstract
Permanent closures and consolidations of public schools are becoming increasingly common in Ontario. This report explores this issue in-depth by examining the potential impacts of a recent decision to close a secondary school in Kingston Ontario, Kingston Collegiate Vocational Institute (KCVI), on the quality of life for residents and neighbourhood well-being and liveability. There is limited empirical evidence on the impacts of school closures on households and neighbourhoods. This report contributes to the gap in research by exposing social impacts experienced by residents currently dealing with the impending closure of their local secondary school, providing an important contribution to the discussion on what considerations should be investigated when determining a school’s future. Further, this report investigates how school asset management can be improved by incorporating more effective collaboration into the decision-making process, reflecting the values and priorities of residents. A multilevel case study was employed using a mixed methods approach to data collection. Using the impending closure of KCVI at the study case, households within the school’s catchment area were surveyed to investigate the potential impacts of the closure at the household level, and key informants were interviewed to investigate the potential impacts at the neighbourhood level. The study findings demonstrate significant concerns about neighbourhood and household well-being and liveability upon the impending closure of KCVI. The decision to close KCVI highlighted three main planning-related conflicts: conflicts with Provincial sustainability policies that place high priority on sustainable land use and development practices to reduce carbon emissions, make efficient use of public infrastructure, and ultimately support the health and well-being of future generations; conflicts with municipality, in terms of missed opportunities to work collaboratively to develop a cohesive plan for the future of the community; and conflicts with the values and priorities of the public, thereby fostering distrust and resentment among residents with the LDSB. The findings strongly indicate that the current school closure process is flawed and does not reflect the values of residents or adequately consider the social well-being of neighbourhoods.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".