Gentrification, Mobility and Automobility; A quantitative study of local driving mode share and commute time in Montréal, Toronto and Vancouver
Bibliographic record
Abstract
This thesis investigates how gentrification affects the transport mode and travel time of commuters in large, globalizing Canadian cities. While there is evidence that gentrification is associated with transit, cycling and walking, gentrification also involves the displacement of low-income and racialized households, and commuters in these households are the least likely to drive to work. Does gentrification contribute to automobility? Also, how are residential mobility and exclusionary displacement related to everyday commute mobility in gentrification? In this dissertation, I cross tabulate commute time and mode by household income, racial status and other sociodemographic characteristics at the census tract scale in 1996, 2006 and 2016. I find that in Montréal, Toronto and Vancouver, gentrification is correlated with a decrease in the mode share for driving among affluent commuters and visible minorities who live in gentrifying neighbourhoods. However, there is no change in the overall percentage of commuters who drive to work, since low-income and racialized minority commuters, who are less likely to drive, are displaced and higher-income and white commuters, who are more likely to drive, become more concentrated in gentrifying places. Also, while within gentrifying places I find little evidence of racial differences in commute time, through gentrification racialized residents are displaced from places where they have a lower driving mode share, and shorter commute times by private vehicle and transit. Moreover, a case study of the Toronto metropolitan area shows that the centralization of households with above average incomes is not on its own associated with a reduction of their private vehicle use over time. At the same time, the private vehicle use of low-income households does not change in gentrifying neighbourhoods, but is bolstered by their increasing concentration in the suburbs. Thus, the cultural aspects of gentrification processes have an important influence on commute behaviour, and these combine with economic change and improvements to local transport infrastructure to support the motility of affluent, white households, while contributing to displacement pressure for low-income and racialized minority households.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".