The Causes and Consequences of School Closures in Inner-City Calgary
Bibliographic record
Abstract
During the early decades of the 2000s, eleven schools closed in Calgary’s established neighbourhoods, due to declining enrolment or outdated infrastructure. The loss of a school is often devastating to a community and can lead to further population decline. A research project was commissioned by the government to investigate the causes and consequences of school closures and to make recommendations, particularly related to urban form, to address the issues. Analysis of the 500 sq km study area included historic evolution of urban form, schools mapping and data analysis, development of a typology of school buildings and grounds, mapping of school inter-relationships and closures, review of school board practices and policies, and precedent studies. A case study of the catchment area of one high school included historic evolution of urban form factors, mapping of the network of feeder schools, analysis of neighbourhood and schools socio-demographics, and a review of school program changes. The causes of school closures relate to a combination of city development processes, neighbourhood lifecycles, neighbourhood types, infill and densification processes, housing types, school sizes and building types, socio-demographic factors influencing school choice, and school board policies, and the consequences affect neighbourhoods and communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".