Bicycles for Mutual Aid: A Participatory Action Research Project with the Toronto Bike Brigade
Bibliographic record
Abstract
The term bicycles for development (BFD) has emerged to consider bicycling for achieving similar goals to sport for development (SFD) or the use of sport as a vehicle for social development (Kidd, 2008). This project builds on previous BFD work by forging novel directions for research on BFD, including: 1) how bicycle-related activities contribute to COVID-19 recovery, specifically in promoting a more environmentally sustainable and equitable world for vulnerable populations; and 2) the ways that cycling can be taken up by QT and BIPOC to challenge existing systems of inequality. Guided by a participatory action research project in partnership with The Bike Brigade, the data collection methods used in this project included arts-based methods and semi-structured interviews. Findings suggested that QT and BIPOC cyclists were taking up bicycling through mutual aid frameworks to support their communities and respond to the COVID-19 pandemic and exacerbated inequality. Further research is needed in the fields of BFD and SFD that: 1) prioritize and center diverse perspectives on bicycling; and 2) engage with creative methodologies such as arts-based approaches.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".