Diverse Food Practices by and for Racialized People in Little Burgundy
Bibliographic record
Abstract
Drawing from a qualitative community-based project in Little Burgundy, a predominantly Black Montreal neighbourhood, this thesis applies community-based and critical ethnographic methods to explain a range of diverse food practices by and for racialized people. Given the changing character of its socio-spatial configurations, this neighbourhood and its history are far more com-plex and multilayered than mainstream narratives would suggest. In this thesis, I explain how individuals depicted as food insecure by community food organizations are in fact involved in diverse food practices rooted in an ethic of mutual care and racial justice while sustaining their cultures and traditions. Rather than attempt to offer a complete story of food landscapes, this thesis provides a glimpse of the diverse and innovative ways racialized people enact community food economies through a series of vignettes situated in four ongoing food projects: a community garden, a food bank, an at-home gardening initiative and a Citizen’s market. At these sites, I turn to the theory of racial viscosity to make sense of how processes of racialization create, obscure, and reinforce lines of separation and belonging in the studied sites. Sketching connections be-tween viscosity, mutual care and racial justice, this research study sheds light on how and why racialization is key to the study of everyday food practices.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".