Life alongside food: Food practices and affective memories amongst immigrant chefs
Bibliographic record
Abstract
Food has great symbolic power in human life, especially for those involved in migratory movements. In contexts of displacement, food performs a bonding role between individuals and their memories, produces a sense of belonging through affective connections between people and places, and operates as a tool for identity negotiation. For professional cooks who immigrate to work in international restaurants, symbolic aspects are not excluded from their daily practices. Besides building relationships with food through professional training and expertise, chefs are people who have chosen to share their lives alongside ingredients and recipes. As such, their practice is informed by various symbolic and cultural aspects that precede the experience inside the professional environment, such as affective memories of homemade cooking and skills learned in the domestic setting. Seeking to explore the symbolic dimensions of culinary practices within narratives of displacement, this paper discusses the results of an ethnographic research developed in 2019 with immigrant chefs in an Argentinian restaurant located in Vitória, Brazil. Guided by Tim Ingold’s notion of life as a trajectory of movement and the Spinozian concept of affect, this paper investigates the role of memories in cooking practices, the influence of domestic practices in professional environments, the food categories chefs create based on their experiences, and the role of affects in professional cooking settings.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".