Bibliographic record
Abstract
Quaker Oats advertising campaigns have long aimed not only at the stomach, but the conscience: “Think you don't have time for a hot breakfast?” asks one. “Think again.” The implication is clear: there is something especially wholesome and nourishing about a piping-hot bowl of oatmeal, and the smiling man in eighteenth century garb offers a product that makes it easy to do what another Quaker ad calls “the right thing to do.” But how can we be sure? What makes food “right”—health effects alone, or ethical/moral considerations about the business of getting that food from the farm to the grocery store?This paper rubs the sleep out of its eyes and takes a closer look at what's in the steaming bowl on the breakfast table. It turns out that a dollar spent on Quaker oatmeal is a dollar in PepsiCo's bank account, and that part of that dollar goes to one of the world's largest suppliers of beef cattle. Meanwhile, it's no coincidence that the suicide rate is climbing among the farmers of the Canadian Great Plains … In the increasingly globalized agribusiness industry, think there's any such thing as a “safe” food? Think again.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.027 |
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".