“I don’t think they care about us”: An urban ethnography of gentrifying leisure spaces in Montréal
Bibliographic record
Abstract
This urban ethnography explores how gentrification is lived in and through everyday spaces of leisure in a divided Montréal neighborhood. The neighbourhood setting is Mountain Sights, situated within the Triangle development—an ongoing urban revitalization project located in the broader neighbourhood of Côte-des-Neiges (CDN). CDN is a historically working-class neighbourhood and home to a large refugee and new immigrant population. I specifically explore how gentrification is played-out in and around De la Savane Park (DSP)—a large urban park situated alongside Mountain Sights Avenue that has long been central to Mountain Sights residents’ leisure practices and community life. My analysis contributes to scholarship on gentrification and critical leisure and sport studies in the following two ways: 1) by highlighting the divisive character of gentrification, which was notable in my ethnographic setting due to an emergent distinction between longer-standing lower-income apartment tenants and higher-income condominium dwellers; 2) by examining how Mountain Sights residents, particularly youth, creatively adapted available (often disinvested) leisure space in and around DSP as “spatial practice”—a theoretical idea coined by sociologist Henri Lefebvre (1991). My thesis concludes with important opening questions that address how the global health crisis incited by the COVID-19 virus has exacerbated the pressures of gentrification on Montréal’s lower-income families
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".