Beyond Biopolitics: Animal Studies, Factory Farms, and the Advent of Deading Life
Bibliographic record
Abstract
This article seeks to do two things: articulate the function of biopolitics as a necessary correlate to human exceptionalism, and argue for the factory farm as a supplementary inverse of biopolitical logic. Human exceptionalism is based fundamentally in a desire to create protected lives, and lives that can be, or even need to be, exterminated. In other words, human exceptionalism is the very definition of biopolitics. However, biopolitical theory was mostly developed around thinking through issues of human genocides, particularly the Nazi Lager. Despite attempts to think the analogies between Auschwitz and a factory farm, such analogies ignore important historical and theoretical specificities. While the biopolitical is an important, even necessary, theoretical understanding of humans’ relations to other animals, it is not sufficient for thinking the realities of factory farming. We need a conceptual apparatus not rooted in the ability to think the horrors of human genocide, but one rooted in the ability to think the horrors of the factory farm. Thus I propose the animals in the factory farm exist in the political ontology of deading life; that is beings who should be alive, but are somehow already dead. This conceptual apparatus is not meant to oppose the thought of the biopolitical, it is meant to supplement the biopolitical—to allow us to think with and beyond the biopolitical.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".