Protein politics
Bibliographic record
Abstract
Powerful actors associated with intensive livestock production are repositioning industrially produced meat and farmed fish as “sustainable protein.” This repositioning, we show, involves justifying the production of meat through a range of metrics, calculations, and valuations. These metrics and associated indicators underpin claims that sustainable protein is more efficient and less wasteful than conventional meat production. Our analysis questions the relationship between efficiency and sustainability in industrial meat production. We show, first, that the industrial meat sector has always focussed on efficiency and the reduction of waste. What is new is that metrics, calculations, and indicators on efficiency and waste reduction are being repurposed and made public to consumers and investors to underpin claims for sustainable and “climate friendly” meat. While this practice is apparent across the animal agriculture sector, it is especially evident in the production of farmed salmon. Our second argument frames sustainable protein metrics as a political logic. While these metrics have been justifiably criticized as a form of environmental “greenwashing” by environmental non-governmental organizations and others, our own critique builds on Cara Daggett’s recent analysis of energy and its political logic. Building on Daggett’s work, we aim to provide a more fundamental critique to the efficiency and waste metrics that are used to support claims for sustainable protein, while simultaneously providing the conceptual and political foundation for more progressive futures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".