Inclusive growth, public transit infrastructure investments and neighbourhood trajectories of inequality in Montreal
Bibliographic record
Abstract
Investing in accessible, affordable and sustainable modes of transportation is increasingly seen as an important policy tool for fostering the development of more inclusive cities and combating the rise in inequality. In this article, we review how the concept of inclusive growth has gained traction at the local level framed within a discourse of building more equitable and sustainable cities with a particular emphasis on transportation infrastructure projects as a way of operationalizing the concept as a policy tool. Using Montreal as a case study, we then proceed to evaluate two competing proposals for major public transit infrastructure projects (the Pink line and the REM Phase II) to see if one may potentially offer more inclusive outcomes in terms of transit access and mobility. We do so by first examining changes in the spatial configurations of neighbourhood income disparities in the city between 1981 and 2016. After identifying a pattern of growing spatial polarization between higher- and lower-income neighbourhoods, we use a buffer analysis of transit stations to assess which of the two proposed transit infrastructure projects is best positioned to curb the growth of neighbourhood disparities. Our results suggest the proposed Pink line project provides more coverage in terms of accessibility and connecting economically disadvantaged neighbourhoods from Montreal Nord to Lachine with the downtown core.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".