Inequalities of extreme commuting across Canada
Bibliographic record
Abstract
There is growing body of research and practice assessing transportation equity and justice. Commuting is an especially important dimension to study since such frequent, non-discretionary travel, can come at the expense of time for other activities and therefore negatively impact mental health and well-being. An "extreme commuter" is a worker who has a particularly burdensome commute, and has previously been defined based on one-way commute times above 60 or 90 minutes. In this paper, we examine the social and geographic inequalities of extreme commuting in Canada. We use a 25% sample of all commuters in Canada in 2016 (n = 4,543,417) and our analysis consists of descriptive statistics and logistic regression models. The average one-way commute time in 2016 across Canada was 26 minutes, but over 9.7% of the workforce had commute times exceeding 60 minutes. However, this rate of extreme commuting was 11.5% for low-income households, 13.5% for immigrants, and 13.4% among non-white Canadians, reaching as high as 18.6% for Black Canadians and 14.7% for Latin American Canadians specifically. We find that these inequalities persist even after controlling for household factors, commute mode, occupation, and built environment characteristics. The persistently significant effects of race in our models point to factors like housing and employment discrimination as possible contributors to extreme commuting. These results highlight commuting disparities at a national scale prior to the COVID-19 pandemic, and represents clear evidence of structural marginalization contributing to racialized inequalities in the critical metric of daily commute times seldom recognized by Canadian scholars and planners.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".