Work Trips: Are There Still Gender Differences? Case of Quebec Metropolitan Area, 1991 and 2001
Bibliographic record
Abstract
Gender differences in work trips have been the subject of an abundant literature from the 70's through the 90's. Recent research indicates that differences between men and women’s trips to work tend to diminish, either measured by number of trips or travel distance/time. The general purpose of this paper is to verify whether gender differences in work trips were still present during the last decade. We use data from the 1991 and the 2001 origin-destination (OD) surveys in the Quebec Metropolitan Area (QMA). A first analysis of the gender differences was realized with comparisons of Euclidean distance between home and workplace controlling for home location in the QMA, presence of children and household’s motorization. Secondly, the distribution of work places was analyzed using centrographic methods in order to compare the activity spaces of male and female workers. Results indicate that gaps between work trip distances of men and women have diminished between 1991 and 2001 in the QMA, and have disappeared for women from two-worker-with-children-and-more-than-one-car households but not for two-worker-with-children households with only one car. The activity areas of men were always larger than those of women particularly for women who live in two-worker-with-children households regardless of the number of cars in the household.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".