Gender Differences in School and Work Commuting Mode Through the Life Cycle: Exploring Trends in the Greater Toronto and Hamilton Area, 1986 to 2011
Bibliographic record
Abstract
Reducing auto dependence and increasing the use of active and sustainable modes of transportation for school and work travel are necessary for alleviating traffic congestion issues that are typical in today’s North American cities and regions. While there is a growing interest in increasing the use of active and sustainable modes of travel for commuter trips in transportation planning, less attention has been paid in practice to gender differences in travel demand. This descriptive study explores gender differences in active transportation, public transit, and automobile use through the life cycle to assess temporal changes in gendered transport over the past 25 years in the greater Toronto and Hamilton area, Canada. Findings suggest that female children and youths are driven to school more frequently than males; however, males drive more than females during the years of labor force participation. Differences between female and male automobile use increase with age, but the gender gap has declined since the mid-1980s. Factors such as having one vehicle per household, more than six household members, and living and working in the city of Toronto are shown to associate with the largest differences in driving between full-time employed women and men. Distances between home and work have increased, particularly for women, and the percentage of women with a driver’s license has increased. Although driving remains higher for men than women during the part of the life course that includes labor force participation, the gender gap in active transportation, public transit, and automobile use appears to be lower today than in the mid-1980s.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".