Bibliographic record
Abstract
Taking everyone by surprise, poutine—an unpretentious Quebecois dish originally made of fries, cheese curds and brown gravy—found its way onto the Canadian State Dinner menu organized by the White House in March 2016. Drawing on my personal relationship with poutine, this paper intends to expose how poutine has managed to enact a form of social mobility. The tasting experience of poutine is first deconstructed through its taste ‘on the tongue’ and its taste as a dynamic social process, to investigate poutine’s palatability and mainstream appeal. Through this tasting analysis, poutine emerges as a new(er) and distinct way to consume food that is increasingly adopted and adapted. A working definition of poutine as a new dish classification label in its own right (just like sandwiches, dumplings, soups, flatbreads or sushi) is proposed. The social mobility of other foods (e.g. lobster, kimchi, garlic, and sushi) is further explored, before discussing how poutine is also connected to a stigma, which weakens the agency of the Quebecois. Using the social identity theory, it appears that Quebecois youth are dismissing this ‘poutine stigma’ through a revaluing approach, which resembles a reappropriation of poutine, not necessarily linguistically (as seen with ‘black’, ‘queer’, or ‘geek’), but rather in a culinary fashion. Coupling poutine’s sociohistorical stigma and its growing Canadization (that is, the presentation, not the consumption per say, of poutine as a Canadian dish), two related situations—the ongoing process of poutine culinary appropriation and the threat of Quebecois cultural absorption by Canadians—are exposed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".