Whose streets? Our streets! Bicibús in Barcelona through a justice lens
Bibliographic record
Abstract
Mobility justice examines how power and injustice shape unequal (im)mobility patterns along gendered, class, and racialized lines. Even grassroots cycling initiatives may be entangled in systemic inequities and mobility injustice. Bicibús is a growing movement of children and adults who go to school together by bikes, skates, or scooters, occupying the streets for safer and healthier cities. We analyze whether and how Bicibús initiatives reflect and reproduce inequalities based on gender, class, or migration in Barcelona. Through interviews with 22 parents, including Bicibús organizers and non-participants, we outline processes of exclusion and inclusion. While we find gender parity, the movement is also comprised mostly of middle-class and white families. The schools that mobilize and benefit are predominately in higher-income neighborhoods, while no routes connect marginalized students from lower-income schools. Barriers to participation include work obligations, materials, confidence and physical abilities, social integration, and logistics. This analysis suggests unequal active mobility to school and biased representation in cycling initiatives. To help address mobility injustice in grassroots cycling initiatives such as Bicibús, we recommend raising awareness about racist and classist inequalities, creating supporting structures, and involving schools to make pro-cycling actions more inclusive and diverse.
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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 | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".