THE PRESENTATION OF THE CHEF IN EVERYDAY LIFE: SOCIALIZING CHEFS IN LIMA, PERU
Bibliographic record
Abstract
ABSTRACT Over the past two decades, Peru has seen a dramatic expansion of restaurants and attention to Peruvian cuisine, a phenomenon known as the "gastronomy boom." Peruvian chefs have become national celebrities, their entrepreneurial and culinary efforts portrayed as a means of transforming Peru into a more prosperous nation. In this paper, based on sixteen months of ethnographic research in Lima, I examine socialization practices in two culinary schools to elucidate how culinary work is linked to person formation in Peru. I show that instructors encourage students to eschew business practices locally classified as vivo (dishonest and crafty) in order to become more orderly. They also instill in students the importance of having the ambition necessary to achieve international prominence. Together, these lessons promote a template for a new, ideal Peruvian citizen whose combination of extroversion and restraint exemplifies Peru’s potential in the global economy.
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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 | 0.002 | 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 | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".