School Lunch is Not a Meal: Posthuman Eating as Folk Phenomenology
Bibliographic record
Abstract
School lunch is one of the least critiqued aspects of compulsory schooling. As a result, there may be a tendency to think of school lunch as part of the hidden curriculum, but what and how students eat are evident and ubiquitous parts of the schooling experience. In demarcating the school lunch as an overt educational event, this article attempts to tell a story behind the centerpiece of that event: meat. We hope to add to the small yet growing body of literature in social foundations of education addressing the multiple meanings and theoretical complexities of school food, as we consider the cafeteria's potential in cultivating posthuman eating through the lens of folk phenomenology. We ask: What are the implications of a site—the school cafeteria—where eating animals is routine and normal, yet still ignored and forgotten? This question extends well beyond the cafeteria itself. Thus, our analysis seeks to make overt a phenomenological reversal that returns to the things themselves—animals (human and nonhuman) and their lives and deaths—as a way to recognize food's posthuman and folk significance. We conclude by linking our analysis to the challenges faced by educators and scholars critiquing the neoliberal school that routinely acts as a training ground for docile bodies and technocratically controlled human and nonhuman subjects. Posthuman eating as folk phenomenology is an opportunity to recover what has been lost in the neoliberal effort to (re)produce students as acquiescent consumers.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.056 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".