The Irreplaceable Frying Pan and the Green-Eyed Tiger: Emotional Transnationalism and the Moving Foodways of Migrants in Montreal
Bibliographic record
Abstract
“Moi, j’suis pas cocorico, j’suis pas fier d’être Français,” Florence, a migrant from France, declared that she does not carry an undying love for her home country. For her, transnational migration is tied to an emotional connection to those who still live France and is bound by her family in Montreal (QC, Canada); it is not restricted by borders or nations, but instead the place where she rests her hat, her conception of ‘home.’ Using oral history interviews, this paper investigates the intersection between emotion, identity, and foodways. The project is a study of métissage that explores the cultural negotiations, preservation, and exchange that occurs when migrants arrived in Montreal in the post 1960 period. The research objective is to study the experience of migration, forced or voluntary, and how the experience may have altered the development and preservation of migrants’ culture and foodways. How have migrant foodways intermingled over time? How does emotion and intimacy shape food processes? In line with the symposium’s theme, the study of migrants’ movement and foodways is at the center of this paper. Many migrants I have spoken to have described their cuisine and culture as tools for navigation, something deeply a part of their lives, yet ever-growing and susceptible to transformation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".