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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".