A ride for whom: Has cycling network expansion reduced inequities in accessibility in Montreal, Canada?
Bibliographic record
Abstract
It is widely agreed today that the existence of a network of bicycle paths fosters a feeling of safety as well as the use of the bicycle for both recreational and utilitarian purposes. Recent studies have found a link between the presence of cycling infrastructures and gentrification. Few studies have however examined the growth of the cycling networks from the perspective of environmental equity. The main objective of this study is to determine whether the extension of the cycling network in the urban areas of Montreal and Longueuil and the city of Laval over a quarter of a century (1991 to 2016) has reduced or reinforced inequities in accessibility for low-income populations, recent immigrants, children, and older people. Archival maps were employed to reconstruct the cycling networks in the Montreal area in a GIS for six years (1991, 1996, 2001, 2006, 2011, 2016). Census data and spatial analysis methods were then used to measure whether or not inequities in the accessibility of the cycling network increased over the period in question. The results show that, in 25 years, the cycling network has more than doubled in size. It can however be seen that some areas are still very poorly served, and that the network lacks connectivity. Low-income individuals have generally enjoyed good accessibility over the entire period. A strong decrease in inaccessibility for recent immigrants and seniors is also observed. The most important result is clearly that there has been little or no improvement for children, who are found to be in a situation of inequity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".