The vegan industrial complex: the political ecology of not eating animals
Bibliographic record
Abstract
Many political ecologists and geographers study ethical diets but most are curiously silent on the topic of death in the food system, specifically what or who is allowed to live and what is let die in the "doing of good." This article aims to show how the practice of eating produces the socio-ecological harm most ethical consumers set out to avoid with their dietary choices. I examine the food systems that produce ethical products for 1) the hierarchical ordering of consumer health in the Global North over the health and well-being of workers in the Global South and 2) how vegetarianism involves the implicit privileging of some animals over others. The article takes take a genealogical approach to the political ecology of food ethics using Black and Indigenous studies in conversation with animal geographies. I draw on Mbembe's (2016) necropolitics, Weheliye's (2014) "not quite human" and Lowe's (2015) critique of humanism to develop a conceptual framework for what lives or dies as a result of ethical dietary choices. I use this framework to examine commodities for the socio-ecological harm that their production extends into the world under the guise of "doing good" or "being ethical." Taking a harm reduction and food sovereignty approach, I advocate for a new ethical framework that includes a limited case for consuming animals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".