“The climate crisis is violence:” sexual and gender-based violence and bicycles for mobility justice and development in Nicaragua <sup>1</sup>
Bibliographic record
Abstract
This paper demonstrates the complexities and challenges of advocating for mobility justice and climate and gender justice within patriarchal, climate-vulnerable contexts such as Southern Nicaragua, and the potential for leisure and sport-focused development programs to support the aims of advocates working across these intersecting areas. Our findings reveal that the bicycle is more than just a tool for mobility; rather, cycling as a leisure practice represents an act of resistance, and bicycle-centric SDP programming provided a pathway for self-identified women to challenge harmful gender norms and address violence to the land. However, while women faced sexual and gender-based violence while cycling, the responsibility for promoting climate justice remained unfairly placed on them through bicycling. A decolonial feminist digital participatory action approach that incorporated photovoice activities, semi-structured interviews, photocollage, sketch maps and participatory GIS, proved essential to amplify the voices of community participants and foster accountability, collaboration and co-learning. Ultimately, this project illustrates the need for more inclusive, community-centred approaches to sport and leisure-focused development programs that prioritise a feminist climate justice approach.Footnote1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".