The vegan industrial complex: the political ecology of not eating animals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it