Is gentrification all bad? Positive association between gentrification and individual’s perceived neighborhood collective efficacy in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Collective efficacy has been associated with many health benefits at the neighborhood level. Therefore, understanding why some communities have greater collective efficacy than others is important from a public health perspective. This study examined the relationship between gentrification and collective efficacy, in Montreal Canada. METHODS: A gentrification index was created using tract level median household income, proportion of the population with a bachelor's degree, average rent, proportion of the population with low income, and proportion of the population aged 30-44. Multilevel linear regression analyses were conducted to measure the association between gentrification and individual level collective efficacy. RESULTS: Gentrification was positively associated with collective efficacy. Gentrifiers (individuals moving into gentrifying neighborhoods) had higher collective efficacy than individuals that lived in a neighborhood that did not gentrify. Perceptions of collective efficacy of the original residents of gentrifying neighborhoods were not significantly different from the perceptions of neighborhood collective efficacy of gentrifiers. CONCLUSIONS: Our results indicate that gentrification was positively associated with perceived collective efficacy. This implies that gentrification could have beneficial health effects for individuals living in gentrifying neighborhoods.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".