Bibliographic record
Abstract
It is no accident that many published articles about food, food processing and food distribution fail to reference the crimes and harms committed by the food industry – that is, the darker side of food (see Holtzman, 2013). Nonetheless, as this volume outlines, there are a myriad of ways in which foodstuffs and food processes are entwined with immoral, unjust, harmful and illegal (in)actions. This chapter focuses on the forms of activism and food movements that react to these crimes and harms, including retort and backlash by food corporations. The first part of the chapter conceptualises the contemporary global food system as the ‘risky food regime’ and outlines its role in the production of food crimes and harms, and the consequences on foodstuffs and consumers. Grounded in this context, the chapter then includes a sketch of how various agents and organisations respond to these problems, including how food corporations counter these food movements and food activism through specific defence strategies. The chapter closes with a discussion of how the ‘food police’ are mitigating – that is, threatening – food choice, and argues that food corporations are simultaneously (and ironically) key facilitators of food crime and the food police.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".