The spatial politics of veganism : "moral branding" in Vancouver's Downtown Eastside
Bibliographic record
Abstract
This paper explores the intersections between the recent rise of veganism into the mainstream and the continued gentrification of low-income and marginalized areas within the urban environment. More specifically, I examine the spatial dynamics of one particular vegan eatery in Vancouver’s Downtown Eastside, how it produces social distance between patrons and DTES residents, thereby reproducing hegemonic power relations, both symbolically and materially. Via ethnographic fieldwork, critical discourse analysis, and engagement with social theory, I highlight how the histories of classism, colonialism, racialization, and othering that the Downtown Eastside was built upon are symbolically reproduced and socially perpetuated via the built environment of the restaurant. Additionally, I examine the restaurant’s usage of “moral branding” and the ways in which this style of branding produces narratives that justify the existence of the space while simultaneously actively erasing its connections to the poverty immediately outside its doors. Ultimately, moralistic vegan branding promotes a decontextualized, ahistorical, capitalistic version of veganism that does not take into account human suffering under industrial meat and dairy production and assumes veganism – in whatever forms it may take – to always be a positive and favorable ethical choice.
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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 | 0.015 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".