Exploring the local impacts of universities on socioeconomic characteristics and housing markets in Canadian urban regions, 1981–2016: A spatial panel modeling approach
Bibliographic record
Abstract
This study examines the spatiotemporal economic and social transformations associated with proximity to major university campuses in Canada’s eight largest urban regions from 1981 to 2016. Using quinquennial census data, we develop spatial panel regression models to analyze four dimensions of neighborhood change at the census tract level: rents, young adult populations, immigrant populations, and bachelor’s degree holders. Our findings reveal that census tracts closer to universities exhibit significantly higher average rents, larger young adult populations, greater immigrant populations, and a higher proportion of university-educated residents. However, we find that these relationships vary greatly over time, indicating more complex dynamics than previously understood. The concentration of young adults, immigrants, and educated individuals near universities has only emerged since the 1980s, while rents in these areas have increased more slowly compared to other metropolitan regions, suggesting convergence rather than gentrification. Additionally, the growing proximity of the immigrant population to universities reflects a longstanding trend rather than a recent development associated with international student enrollment. These results highlight the dynamic nature of university-neighborhoods’ relationships and underscore the significance of these institutions in shaping the economic and social geographies of Canadian urban regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".