Gender differences in urban recreational running: A data-driven approach
Bibliographic record
Abstract
Exploring the dynamics of urban recreational running, this study examines the spatial and temporal patterns of running activities among men and women in two major North American cities, Montréal, Canada and Washington, DC, USA. A total of 20,446 running trajectories from a geosocial fitness tracking application were analyzed, revealing significant gender differences. These gender preferences differ in terms of location and time, highlighting significant variations between the two cities and shifts between day and night running habits. We further investigate the influence of socio-economic, demographic, and built environment factors on these different spatiotemporal patterns. Regression models show that proximity to bike lanes and parks strongly influenced running locations in both cities, with a preference for lower population density and lower median household income areas. Insights from this work are important for urban planners and public health officials, providing a data-driven foundation for developing more inclusive and safe public spaces for recreational activities. The study not only contributes to our understanding of urban recreational behaviors but also addresses broader societal concerns about gender and public space utilization.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".