Bibliographic record
Abstract
This article develops an anthropological understanding of the intersection between food and culture in Edmonton’s food truck industry. More specifically, I explore how Edmonton food trucks are able to connect local and global cuisines and cultures through the menu items they offer and images they present to customers, which are predominantly influenced by local, ethnic, authentic and fusion creations. I gained data for this study by employing an ethnographic methodology and relational approach, which involved conducting semi-structured interviews with Edmonton food truck vendors and customers, and engaging in participant observation from May through August of 2019. The following food trucks serve as case studies in my research: Explore India, Dosi Rock, Dedo’s Food Truck and Catering, Meat Street Pies and The Dog. My findings reveal how advertising themes common to Edmonton food trucks, which include notions of authenticity, traditionalism and high quality ingredients, contribute to the construction of a cultural “Other” for customer consumption. In addition, my findings reveal how Edmonton food truck vendors are inspired to develop menus and dishes rooted in and inspirited by their cultural heritages, transnational identities, world travels and movement across ethnoscapes. In conclusion, I argue that the globally inspired ethnocultural cuisines offered by Edmonton food truck vendors are “localized” in a variety of meaningful ways. This study contributes to an underrepresented literature on street food vending in Edmonton by analyzing how food truck move through, occupy, and create urban spaces in meaningful ways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".