Is Canada’s commuter bicycling population becoming more representative of the general population over time? A national portrait of bicycle commute mode share 1996–2016.
Bibliographic record
Abstract
Despite equity gaining increasing attention in Canadian bicycling practice and research, the demographics of who is bicycling have not been documented nationally in Canada. This study uses Canadian census data to provide a nationwide portrait of: 1) how bicycle commute mode share varies by gender, race, income, and age in Canada; 2) how the sociodemographic characteristics of bicycle commuters in Canada have shifted between 1996 and 2016; and 3) how bicycle commuting and the demographics of bicycle commuters vary across metropolitan regions in Canada. We find that men, people who are not visible minorities and low-income populations commute by bicycle at double the rates of women, visible minorities, and other income groups, respectively. Women comprise an increasing share of bicycling commuters over the 20 years, whereas bicycling is increasing at similar rates across race and income groups. Cycling distinctly decreases with age. Cycling rates vary by region and there is some evidence that low-income and visible minority groups bicycle more in smaller, more car-centric metropolitan areas. These findings identify differences in bicycling across socio-demographic groups and geographic regions, which sets a foundation for research to uncover why these differences are occurring, in order to point policymakers toward targeted solutions that specifically address inequities in bicycle commuting between population groups.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".