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Breach, Bridgehead, or Trojan Horse? An Exploration of the Role of Food Trucks in Montreal’s Changing Foodscape

2017· book-chapter· en· W2803685701 on OpenAlexaboutno aff
Alan Nash

Bibliographic record

VenueThe MIT Press eBooks · 2017
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTruckViewpointsTrojan horseAdvertisingFood studiesBusinessMarketingEngineeringPolitical scienceLawComputer securityComputer scienceArt

Abstract

fetched live from OpenAlex

After a seventy-year ban, the Canadian city of Montreal began a pilot program in 2013 that permitted a limited number of food trucks back onto its streets. Such an experiment offers opportunities to re-evaluate the “traditional view” that food trucks are part of an informal economy in which new arrivals to a city can create jobs for themselves, and the contrary positions that newer “foodie” food trucks have either opened a “breach” with that informal economy by catering only to the more affluent, or offer instead a “bridgehead” by bringing newer cuisines to the street where everyone can experience them. This paper explores the merits of these viewpoints through analysis of the long-running debate over Montreal’s ban on food trucks; the pilot program’s regulations permitting such trucks back into the city; a survey of the goals of the trucks’ operators; and, fourthly, an analysis of customer reviews on the social media site Yelp. This evaluation concludes that Montreal’s food truck experiment should not be interpreted as either a “breach” or a “bridgehead” – rather, it represents the use of food trucks as a pawn (or “Trojan horse”), in a much larger strategy by city planners to promote Montreal through “place branding”.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.236
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreOther

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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