Bibliographic record
Abstract
This dissertation examines suburbanization of poverty in Canadian cities during the late 20th and early 21st centuries, with particular focus on its relation to the distribution of transportation infrastructure and travel behaviour outcomes. The body of this dissertation consists of a literature review followed by three quantitative research papers. The first examines suburbanization of poverty in Toronto over a 25 year period relative to changes in public transit accessibility and adverse travel behaviour outcomes (e.g. longer commute times). The second uses panel data to analyze and tabulate individual pathways to suburban poverty across Canada. And the third directly asks whether low-income residents are disproportionately moving away from public transit. Findings show that many suburban areas are not only declining in socio-economic status, but are also experiencing worsening travel restrictions, evidenced by longer commute times and lower activity participation rates. Importantly, the primary pathway to suburban poverty is sourced from residents dropping into poverty in the suburbs, rather than from moving away from central areas or due to immigration. Moreover, while low-income residents reduce their level of transit accessibility when they move, they are not doing so at a greater rate than higher-income movers. Overall, this research generates important knowledge about the changing structure of urban neighbourhoods while also providing pertinent information to aid preventative policy aimed at reducing suburban poverty in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".