Food Trucks, Cultural Identity, and Social Justice
Bibliographic record
Abstract
Aspects of the urban food truck phenomenon, including community economic development, regulatory issues, and clashes between ethnic authenticity and local sustainability. The food truck on the corner could be a brightly painted old-style lonchera offering tacos or an upscale mobile vendor serving lobster rolls. Customers range from gastro-tourists to construction workers, all eager for food that is delicious, authentic, and relatively inexpensive. Although some cities that host food trucks encourage their proliferation, others throw up regulatory roadblocks. This book examines the food truck phenomenon in North American cities from Los Angeles to Montreal, taking a novel perspective: social justice. It considers the motivating factors behind a city's promotion or restriction of mobile food vending, and how these motivations might connect to or impede broad goals of social justice. The contributors investigate the discriminatory implementation of rules, with gentrified hipsters often receiving preferential treatment over traditional immigrants; food trucks as part of community economic development; and food trucks' role in cultural identity formation. They describe, among other things, mobile food vending in Portland, Oregon, where relaxed permitting encourages street food; the criminalization of food trucks by Los Angeles and New York City health codes; food as cultural currency in Montreal; social and spatial bifurcation of food trucks in Chicago and Durham, North Carolina; and food trucks as a part of Vancouver, Canada's, self-branding as the “Greenest City.” Contributors Julian Agyeman, Sean Basinski, Jennifer Clark, Ana Croegaert, Kathleen Dunn, Renia Ehrenfeucht, Emma French, Matthew Gebhardt, Phoebe Godfrey, Amy Hanser, Robert Lemon, Nina Martin, Caitlin Matthews, Nathan McClintock, Alfonso Morales, Alan Nash, Katherine Alexandra Newman, Lenore Lauri Newman, Alex Novie, Matthew Shapiro, Hannah Sobel, Mark Vallianatos, Ginette Wessel, Edward Whittall, Mackenzie Wood
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".