Bibliographic record
Abstract
To write about food-related nostalgia is to wade into a cultural ocean of common sense, cliché, and iconic narrative: Proust’s madeleines, of course; Seders; soul food; mom’s cooking; any and all gustatory lifelines to what was once home. Food and memory are, after all, inextricable, even across species lines; most sentient beings actively and continuously need to inhabit the intersection of food and memory in order to survive at all. In this chapter I will compare two different modes of political survival and their reliance on collective memories of food: on the one hand, the corporate food system makes heavy use of nostalgic advertising to keep business profitable, and, on the other, burgeoning vegan foodways are turning to food stories in order to create a sense of community and shared identity. As usual, the political survival of systems and stories has biopolitical consequences: the lives of billions of animals are at stake in the narratives we choose to live by and the infrastructure we build on those narratives. Given this fact, we need a more robust account of the food memories we are currently being invited to identify with. It is becoming increasingly clear that veganism not only needs to persuade rationally or make ethical appeals—it also needs to tell stories and make memories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".