Meat-ing Demand: Is In Vitro Meat a Pragmatic, Problematic, or Paradoxical Solution?
Bibliographic record
Abstract
Even in the midst of an ecological crisis, population and income continue to increase and so too does the global appetite for meat. One response by scientists has been to work towards making in vitro meat (IVM) a commercial reality, which would allow meat to be produced on a large scale without the husbandry and slaughter of enormous numbers of animals, as under the current industrial meat production system. Proponents of IVM technology claim that it could cut hunger, offer public health benefits, mitigate the environmental effects of conventional industrial meat production, and improve animal welfare. However, taking a critical, ecofeminist perspective on IVM highlights the need to assess not only the technical attributes and possibilities of the technology but also its underlying worldview as well as the unintended social and environmental consequences that could result. Reflecting on the question of whether IVM is a pragmatic, problematic, or paradoxical solution to the ills associated with industrial meat production and increasing meat consumption, this article argues that optimistic claims trumpeting the promissory potential of IVM are over-simplistic and warrant closer scrutiny. The importance of careful deliberation on the implications of emerging technologies like IVM cannot be understated because how the ethical discourse unfolds in the early stages will be significant in influencing public perception and social acceptance as well as shaping policy and regulatory design.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.009 |
| 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".