‘Cultured’ Food Futures? Agricultural Power, New Meat Ontologies, and Law in the Anthropocene
Bibliographic record
Abstract
Animal agriculture in the US and Canada is a colonial geography borne of imported ontologies of property, life, land, and food shaped by and reproducing agricultural power. This article primarily examines the ontologization of in-vitro meat (IVM) and, to a lesser degree, plant-based synthetic meat relative to our current food ontologies. IVM is positioned as the pragmatic solution to food-driven climate catastrophe in that it will supposedly allow consumers to eat meat without the ethical, environmental, safety, or health concerns associated with agriculturally produced meat. I show that arguments for and against new meat technologies pivot on ontological claims about its realness. Those in favour claim that ‘real meat’ is nothing more than a specific chemical composition that can be divorced from the animal body and current production methods. Those against IVM claim that it cannot be separated from meat as the fetishization of meat renders these technologies intelligible in the first place, and that current production methods rely on ‘livestock’ and the slaughterhouse. IVM then represents a modified form of agricultural power in which the point of application moves from the animal body to the animal cell, and synthetic meat is an articulable invention due to the material and symbolic place of animal flesh in colonial orderings of life. The regulation of these new meat technologies will likely continue to ontologize farmed animals as meat, thereby continuing dominant relationships between agricultural power and food law. I conclude by considering whether new meat technologies ought to be ontologized as food.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.099 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".