What drives the gender-cycling-gap? Census analysis from Ireland
Bibliographic record
Abstract
Switching from car to bicycle would reduce the environmental impact of personal travel and improvethe physical health of commuters. Cycling rates have been increasing in Ireland over the last tenyears, but there is now a large difference in maleandfemale participation-only about a quarter of cyclists on Irish roads are female. This paper explores what factors drive this "gender-cycling-gap",with the goal of identifying policies which will make cycling more accessible to females as a means of commuting. We combine the latest census data with ageospatialsurvey on cycle lane densityforDublin, and apply standard regression techniques. Specific attention is given to the role of cycling infrastructurein increasing participation rates.For both males and females, increased distance to city, living in an apartment and having young children reduce participation rates, while education has a strong positive effect. However, the effects of education, distance and apartment living areconsiderablystronger for women. Furthermore, areas with higher shares of female professionals, controlling for other factors, are found to have lower participation rates. We find no relationship between the provision of cycling infrastructure and participationwhich suggests that the provision of cycle lanes in their current form are possibly not meeting the needs of potential new cyclists and providing access to the key employment areas within the city.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".