Peddlers or Pedalers? The Bicycle Trades and Street Redesign Policies of Mexico City
Bibliographic record
Abstract
The academic and practitioner literature on urban cycling is principally geared towards the promotion of the bicycle as a sustainable mode of commuting. This literature disregards cycle users who ride out of necessity, especially those who employ bicycles, tricycles, and other cycles as an integral part of their trades. These bicioficios or bicycle trades are conspicuously absent from urban mobility policies in cities where they abound, particularly in the Global South, and are perceived by mainstream cycling advocacy as either incidental cyclists or by urban planners as variations of the informal street vending landscape.Little has been studied about these bicycle trades, such that my research is exploratory and qualitative in nature. Drawing from a blend of ethnographic data, expert interviews, and document analysis conducted between 2017 and 2020 in Mexico City, I aimed to examine the sociospatial practices of these itinerant workers. I employed a poststructural policy analysis of the data, aimed at understanding how urban cycling and street redesign policies come to terms with the actualities of Mexico City’s bicycle trades. Key results of this study are presented in three chapters that engage with current discussions on cycling affordances, invisible cyclings, social practice theory, environmental gentrification, and marginality. The case of Mexico City’s bicycle trades demonstrates how unconventional cycling practices such as cargo, deliveries, servicing, and vending are rendered invisible by local advocacy despite their roles in sustaining urban livelihoods. I also find that bicycle traders present spatial, social and political marginalities: they use a bicycle, but they are not cyclists; they are mobile but cannot remain on the street; they are culturally appreciated but are not fully recognized by the policies of their streets. Thus, this thesis addresses normative and conceptual gaps in understanding these contradictions, highlighting the need to widen the conceptual scope of what urban cycling can be. Ultimately, this doctoral dissertation helps reframe urban cycling as more than a lifestyle choice that brings about socioeconomic benefits, and beyond the confines of transport studies, towards the more grounded goals of mobility justice and the recognition of everyday livelihood cyclings in the redesign of urban streetscapes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".