‘Bicycles are really important for women!’ Exploring bicycles, gender and development in Nicaragua and Uganda
Bibliographic record
Abstract
This article explores ‘bicycles for development’ (BFD) – a ‘movement’ that positions the bicycle as a tool to promote key development goals, especially those related to the achievement of gender equality. Despite the increasing growth and prominence of BFD, there remains limited empirical research that investigates the intersections among gender, development, mobility and technologies such as the bicycle. Using visual participatory action research – informed by postcolonial feminist theory and new materialisms – this study explored how bicycles shaped the lives of women and girls in both structured BFD programmes (Uganda) and recreational cycling environments (Nicaragua). Three interrelated themes are discussed: (1) within communities there are conflicting views of the women and girls who participate in BFD and broader cycling related activities; (2) women in this study, through their involvement in BFD programmes or their engagement in cycling, challenge gender norms and resist traditional gender stereotypes related to cycling; and (3) access to a bicycle is associated with a focus on domestic and income-generating work – (re-)producing the burden on women to be primary caregivers. We conclude by reflecting on the duality of the bicycle as a promising and intricate technology used to contribute to gender and development objectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".