Gentrification, neighborhood socioeconomic factors and urban vegetation inequities: A study of greenspace and tree canopy increases in Montreal, Canada
Bibliographic record
Abstract
This study investigates the relationship between increasing urban vegetation and census tract-level green inequities, as well as the role of social indicators in this relationship. We analyzed the augmentation of greenspace and tree canopy in Montreal, Canada, between 2011 and 2017, and its effect on green inequities based on material deprivation, the percentage of visible minorities, and gentrification status using Poisson spatial random effect models. Our analyses showed an increase in greenspace from 57.4% to 65.8% and tree canopy from 21.1% to 22.3% between 2011 and 2017. Census tracts (CTs) with higher levels of material deprivation or a higher percentage of visible minority population had less greenspace and tree canopy at baseline in 2011. Additionally, CTs that were not gentrified had less greenspace and tree canopy than ineligible for gentrification CTs. Furthermore, CTs with more visible minorities, higher levels of material deprivation, or those that did not gentrify had smaller increases in greenspace and tree canopy between 2011 and 2017. Among CTs with more visible minorities or higher levels of material deprivation, those with greater greenspace and tree canopy at baseline also experienced greater increases during the study time period. Conversely, among ineligible for gentrification CTs, those with less greenspace/tree canopy at baseline experienced greater increase in greenspace/tree canopy. Our analysis revealed that despite an increase in urban vegetation, inequities in urban vegetation persists. To reduce green inequities and promote social equity in this particular study area, urban planning policies should prioritize CTs with higher levels of material deprivation, more visible minorities, or those that did not gentrify, and focus on increasing urban vegetation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".