Associations between gentrification, census tract-level socioeconomic status, and cycling infrastructure expansions in Montreal, Canada
Bibliographic record
Abstract
Background: Cycling infrastructure investments support active transportation, improve population health, and reduce health inequities. This study examines the relationship between changes in cycling infrastructure (2011-2016) and census tract (CT)-level measures of material deprivation, visible minorities, and gentrification in Montreal. Methods: Our outcomes are the length of protected bike lanes, cyclist-only paths, multi-use paths, and on-street bike lanes in 2011, and change in total length of bike lanes between 2011 and 2016 at the CT level. Census data provided measures of the level of material deprivation and of the percentage of visible minorities in 2011, and if a CT gentrified between 2011 and 2016. Using a hurdle modeling approach, we explore associations among these CT-level socioeconomic measures, gentrification status, baseline cycling infrastructure (2011), and its changes (2011-2016). We further tested if these associations varied depending on the baseline level of existing infrastructure, to assess if areas with originally less resources benefited less or more. Results: In 2011, CTs with higher level of material deprivation or greater percentages of visible minorities had less cycling infrastructure. Overall, between 2011 and 2016, cycling infrastructure increased from 7.0% to 10.9% of the road network, but the implementation of new cycling infrastructure in CTs with no pre-existing cycling infrastructure in 2011 was less likely to occur in CTs with a higher percentage of visible minorities. High-income CTs that were ineligible for gentrification between 2011 and 2016 benefited less from new cycling infrastructure implementations compared to low-income CTs that were not gentrified during the same period. Conclusion: Montreal's municipal cycling infrastructure programs did not exacerbate socioeconomic disparities in cycling infrastructure from 2011 to 2016 in CTs with pre-existing infrastructure. However, it is crucial to prioritize the implementation of cycling infrastructure in CTs with high populations of visible minorities, particularly in CTs where no cycling infrastructure currently exists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".