Bibliographic record
Abstract
Transport cycling uptake is on the rise in many cities; in Toronto, Canada, cycling is the fastest growing mode of transportation. In many of these cities there is evidence that cycling participation rates are not distributed equally across the population. Notably, a gender-gap in cycling has been observed in many cities with low cycling rates, including Toronto, whereby approximately two thirds of commuter cyclists identify as men and one third identify as women. This thesis is concerned with gender and cycling. Drawing from perspectives from feminist geography, this research examines how the embodied experience of cycling shapes, and how is it shaped by, intersecting axes of identity. A critical literature review of articles concerned with gender and cycling finds that two hypotheses are commonly explored to explain the gender-gap in cycling: (1) that women cycle less than men due to greater concerns over safety and (2) due to their tendency to complete more household-serving travel, a type of travel said to be more challenging to do by bike because it often involves carrying goods and/or children. The social factors underpinning these trends, as well as the ways in which other axes of identity intersect with gender to shape cycling behaviours is lacking from the current literature. This research aims to address this research gap by providing a feminist geography of cycling. To do so, a research project was completed in collaboration with Bike Host, a cycling mentorship program targeting immigrants and refugees in Toronto, Canada. Amongst other research activities, semi-structured interviews were completed with participants to explore the embodied experience of cycling. Key results from this study are presented in three chapters. The first examines the gendered and classed embodied practices that shape and are shaped by cycling. Then, the social, temporal, and spatial dimensions of many different types of fear of cycling are explored. Finally, the ways in which participants used bicycles to complete household-serving travel, a gendered mobility, are reported. Taken together, this dissertation demonstrates the role patriarchal and classist power relations play in shaping who cycles.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".