Bibliographic record
Abstract
A consumer's guide to the food system, from local to global: our part as citizens in the interconnected networks, institutions, and organizations that enable our food choices. Everybody eats. We may even consider ourselves experts on the topic, or at least Instagram experts. But are we aware that the shrimp in our freezer may be farmed and frozen in Vietnam, the grapes in our fruit bowl shipped from Chile, and the coffee in our coffee maker grown in Nicaragua, roasted in Germany, and distributed in Canada? Whether we know it or not, every time we shop for food, cook, and eat, we connect ourselves to complex supply networks, institutions, and organizations that enable our food choices. Even locavores may not know the whole story of the produce they buy at the farmers market. In this volume in the MIT Press Essential Knowledge series, food writer and scholar Fabio Parasecoli offers a consumer's guide to the food system, from local to global. Parasecoli describes a system made up of open-ended, shifting, and unstable networks rather than well-defined chains; considers healthy food and the contradictory advice about it consumers receive; discusses food waste and the implications for sustainability; explores food technologies (and “culinary luddism”); and examines hunger and food insecurity in both developing and developed countries. Parasecoli reminds us that we are not only consumers but also citizens, and as citizens we have more power to improve the food system than we do by our individual food choices.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.582 | 0.409 |
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".