“Foucault and Critical Animal Studies: Genealogies of Agricultural Power”
Bibliographic record
Abstract
Abstract Michel Foucault is well known as a theorist of power who provided forceful critiques of institutions of confinement such as the psychiatric asylum and the prison. Although the invention of factory farms and industrial slaughterhouses, like prisons and psychiatric hospitals, can be considered emblematic moments in a history of modernity, and although the modern farm is an institution of confinement comparable to the prison, Foucault never addressed these institutions, the politics of animal agriculture, or power relationships between humans and other animals more generally. The few times that Foucault discussed animality or human–animal relations, animals and animality remained metaphors for humans and human experiences. Despite Foucault's failure to analyze human–nonhuman animal relations, a significant body of Critical Animal Studies literature has mobilized Foucault's work over the last decade. In particular, a number of scholars have taken up Foucault's writings to consider how relations between humans and nonhuman animals in agriculture might be conceptualized as instances of sovereign power, biopower, disciplinary power, and pastoral power, as well as why we may not think that these are power relations at all. This essay provides an overview of Foucault's accounts of power and of the Foucauldian scholarship that applies these accounts to human–nonhuman animal relations in animal agriculture.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.092 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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 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".