On the Intertwining of Cellular Agriculture and Animal Agriculture: History, Materiality, Ideology, and Collaboration
Bibliographic record
Abstract
This review essay documents continuities between (industrial) animal agriculture and cellular agriculture and raises key questions about whether or not the technology might be able to deliver on its promise of food system transformation. It traces how industrial history, connections to the livestock industry, and disavowal are extended through the innovation of cellular agriculture. In particular, it is shown that cellular agriculture has had connections to (industrial) animal agriculture since its very beginning and at nearly every step since then. I argue that cellular agriculture can be positioned as the epitome of (industrial) animal agriculture in terms of history, material practices, and ideology. Such a critique of cellular agriculture has become somewhat commonplace but while a number of papers have raised similar concerns individually, there exists no sustained focus on such similarities to make this point holistically. Such connections are important in framing the future of cellular agriculture and the fate of farmed animals and the environment. Carefully considering the continuities between cellular agriculture and animal agriculture is crucial when considering whether promoting cellular agricultural is a prudent approach to addressing problems associated with animal agriculture. The cumulative number and extent of connections covered in this essay leads to questions of who will benefit with the advent of cellular 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".