The temporal sequence between gentrification and cycling infrastructure expansions in Montreal, Canada
Bibliographic record
Abstract
Increases in cycling infrastructure might be linked to gentrification. However, there is little empirical evidence investigating the existence and directionality of this possible relationship. This study examined the temporal sequence involved in the relation between gentrification and increases in the cycling infrastructure in Montreal, Canada. We analyzed changes in cycling infrastructure between 2006, 2011, and 2016, considering cyclist-only paths, multi-use paths, and on-street bike lanes. The Ding measure was used to identify gentrified census tracts (CTs) using census data. We implemented logistic regression models with and without geographically weighted regression specification at the CT level to test three scenarios; whether an increase in cycling infrastructure (2006–2011) was associated with subsequent gentrification (2011–2016); whether gentrification (2006–2011) was associated with subsequent increase in cycling infrastructure (2011–2016); or if these phenomena happened simultaneously (2011–2016). Increase in cycling infrastructure was not linked to subsequent gentrification, nor did these two phenomena happen simultaneously. However, gentrified CTs had a 44% greater chance of a subsequent increase in cycling infrastructure, with varying strengths of associations across the study area. When planning increases in cycling infrastructure, it is crucial to take an equity-based approach that underlying sociodemographic dynamics of urban CTs. To achieve this, cities need to engage in broad upstream community engagement, ensuring the inclusion of a diverse range of voices in the decision-making process.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".