Understanding and Practicing Ethical Gastronomy: How to Use Life of Pi in Undergraduate Courses
Bibliographic record
Abstract
Following Brillat-Savarin’s aphorism, “You are what you eat”, many contemporary literary scholars and college professors have been increasingly drawn to food-centric studies. They seek to articulate and develop the profound connection between food consumption and personal identity. Remarkably, in the USA and Canada, some scholars advocate for literature courses that combine literary analysis with immersive experiences like food-oriented field trips, cooking classes, and communal dining. These courses allow students to explore the intricate relationship between food, culture, environment, politics, and economics. This paper asserts that by participating in such courses, college students can derivate intellectual enrichment and practical skills in the culinary arts. Furthermore, these courses encourage students to consider their connections to the place and environment where the food was made. With the aim of inspiring the design of a creative and interdisciplinary curriculum for undergraduate students, this paper proposes that Yann Martel’s 2001 novel Life of Pi as a potential text suitable for food-oriented classes. It demonstrates that Life of Pi is, in essence, a food narrative that underscores the importance of physical and spiritual nourishment. Additionally, it outlines a pedagogical approach for using Life of Pi in a classroom setting that seamlessly blends literary study and culinary projects.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".