Creating Sustainable Cities through Cycling Infrastructure? Learning from Insurgent Mobilities
Bibliographic record
Abstract
As policy makers grapple with rapid motorization processes, cycling facilities are gaining new urgency, offering non-polluting and affordable alternatives to automobility. At the same time, urban sustainability paradigms tend to focus on purely technical solutions to transportation challenges, leaving questions of history and social power aside. Drawing from ethnographic fieldwork in Aguascalientes Mexico, this article contributes to the transportation and mobility justice literature by focusing on the work of social movements in confronting a variety of challenges in the provision of active-transportation services. First, this research explores how social movements express and negotiate transportation-justice concerns to government and planning authorities. Next, I build on the concept of insurgent citizenship to highlight the processes through which residents contest ongoing injustices and formulate alternatives for building inclusive cities. From the creation of makeshift cycling lanes in underserved urban areas to the search for socially just alternative to policing, social movements are forging new pathways to re-envision sustainable transportation systems. These insurgent forms of citymaking—understood here as insurgent mobilities—underscore the creative role of citizens in producing the city as well as the enormous amount of care work involved in these processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".