Exploration de la relation entre la proximité géographique des universités et la transformation des quartiers dans les agglomérations canadiennes entre 1981 et 2016 : une analyse par régressions spatiales par panel
Bibliographic record
Abstract
Depuis les années 60, les universités nord-américaines ont joué un rôle important dans le développement économique des villes. Elles se positionnent comme d’importants employeurs et dynamisent l’économie locale, en attirant de nombreux étudiants et en ajoutant de la valeur au marché local. Toutefois, elles peuvent également générer des effets négatifs, tels que la gentrification, la « studentification » et la « youthification », entraînant des modifications dans la démographie urbaine. Ces phénomènes se manifestent par une hausse des loyers, des déplacements de populations vulnérables et le déclin physique des quartiers. Dans le contexte canadien, bien que ces impacts aient été mis en lumière par des recherches antérieures, la plupart de ces études se concentrent sur un nombre limité de villes et sur une courte période. Cette étude vise à examiner la relation entre la proximité des universités et les transformations urbaines dans l’ensemble du système urbain canadien, entre 1981 et 2016. Elle se base sur des données de recensement et applique des modèles de régression spatiale par panel qui permettent d’étudier ces phénomènes sur le long terme, en contrôlant l’autocorrélation spatiale et les effets fixes. Les résultats suggèrent des tendances systémiques observées dans les grandes régions métropolitaines et révèlent que ces tendances ne se limitent pas à quelques villes du Canada. La proximité des universités coïncide avec des loyers plus élevés, une plus grande proportion de jeunes adultes, une concentration d’immigrants et de titulaires de diplômes de baccalauréat ou plus. Ces concentrations indiquent l’exclusion des populations défavorisées et moins aisées. L’identification de ces exclusions pourrait aider les urbanistes à développer des villes plus inclusives et à établir des relations université-communauté plus équitables. \n \nSince the 1960s, North American universities have played a significant role in the economic development of cities. They position themselves as major employers and boost the local economy by attracting numerous students and adding value to the local market. However, they can also generate negative effects, such as gentrification, "studentification," and "youthification," leading to changes in urban demographics. These phenomena manifest as an increase in rents, displacement of vulnerable populations, and the physical decline of neighborhoods. In the Canadian context, although these impacts have been highlighted by previous research, most of these studies focus on a limited number of cities and over a short period. This study aims to examine the relationship between the proximity of universities and urban transformations across the entire Canadian urban system, between 1981 and 2016. It is based on census data and applies spatial panel regression models that allow for the study of these phenomena over the long term, controlling for spatial autocorrelation and fixed effects. The results suggest systemic trends observed in large metropolitan areas and reveal that these trends are not limited to a few cities in Canada. The proximity of universities coincides with higher rents, a higher proportion of young adults, a concentration of immigrants, and holders of bachelor’s degrees or higher. These concentrations indicate the exclusion of disadvantaged and less affluent populations. Identifying these exclusions could help urban planners develop more inclusive cities and \nestablish more equitable university-community relationships.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".