Before displacement: studentification, campus-led gentrification and rental market transformation in a multiethnic neighborhood (Parc-Extension, Montréal)
Bibliographic record
Abstract
This article explores a case of campus-led neighborhood change that weaves together an analysis of gentrification, studentification and displacement. Contributing to the understanding of displacement pressure, this empirical study employs a temporal perspective and an innovative mixed method that captures the shifting state of the rental market and the perceptions of neighborhood change as understood by immigrant and low-income residents of Parc-Extension. We analyze how studentification is promoted in a campus-led gentrification case study, showing how both gentrification and studentification participate in the rise of evictions and displacement pressures for long term residents. By documenting the residential experience in rental housing through semi-structured interviews and data mining of rental listings on a popular platform in Canada (Kijiji), we propose an empirical perspective on displacement pressure and contribute to the development of this concept in gentrification and studentification studies. The article begins by reviewing the literature on gentrification-induced displacement, displacement pressure, state-led gentrification and studentification. This is followed by contextualizing our Montréal case study. We then outline our mixed methodologies and explain our data collection by web-scraping and fieldwork modalities. Finally, we discuss our results showing how the mechanism of displacement pressure can be linked with studentification and new-build, campus-led gentrification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".