9 Food as Charity, Community-Building, and Cosmopolitanism on a Budget
Bibliographic record
Abstract
Critics of mainstream multiculturalism such as urban geographers Kanishka Goonewardena and Stefan Kipfer rightly criticize what they call the "food and festivals" brand of "aestheticized difference." Driven by corporate interests, public-private city-branding efforts, "creative class" chatter, and tourism strategies, such marketing, they note, seeks to sell diversity through superficial or reductionist notions of difference based on the "exotic" pleasures of "visible" and "edible" ethnicity.1 However, we should not categorically dismiss every form of culinary pluralism as suspect or exploitative as there are community and collaborative contexts in which the consumption of "ethnic" foods can have a more positive impact on those involved.The International Institute of Metropolitan Toronto used food as a way of promoting immigrant integration and liberal cosmopolitanism in three major ways.One was in social welfare practice aimed at supporting struggling families and avoiding mass maladjustment.Another strategy entailed the group dinners and collaboratively organized banquets meant to foster community through cross-cultural sharing and exchange in festive contexts.As a third strategy, the cookbook projects, like the banquets, bore elements of popular and tourism-oriented spectacle (see chapter 10), as evidenced by the many references to colourfully decorated tables and enticing ethnic dishes.Each type of activity differed in terms of its potential for fostering a cultural pluralism rooted in meaningful cross-cultural interactions and social relationships.This chapter highlights the role of food in promoting Institute-style pluralism.The discussion of the mix of Anglo, ethno-Canadian, and immigrant women most directly involved in these activities is informed by the literature that explores the multifaceted character of food as material resource, political tool, social practice, cultural marker, and site of contest and negotiation between dominant and less powerful groups.2 The women in question included the Institute's female group workers and counsellors, and the volunteers recruited through the Catholic Women's League, IODE, Toronto Junior League, Chapter NineFood as Charity, Community-Building, and Cosmopolitanism on a Budget
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".