Bibliographic record
Abstract
Mainstream environmentalism has long prioritized wild animals and their habitats while paying little attention to the explosive growth of global livestock production and consumption. However, this blind spot to livestock is changing quickly, in large part because of the rising general awareness of the resource and emissions intensity of animal-based foods and how it relates the interwoven crises of climate change and biodiversity loss. This paper considers both the fertile ground for animal advocacy to be found in the mounting scientific evidence about environmental inefficiencies of animal-based foods, and the need to be attentive to the risks it bears. The principal danger of efficiency-centred narratives is that if they are largely focused on climate change and biodiversity loss, the goal of reducing relative associated impacts can appear in a way that helps to further stoke the growth of industrially produced birds, which should be understood in relation to the already well-established poultrification of global livestock supply and demand. This paper highlights the importance of challenging this partial lens and response, and stresses the need to connect macro-scale environmental concerns to critical reflection about the ways that animal lives are organized in industrial livestock production. The concern for declining wild animal populations among environmentalists is a key lever for this, as industrial livestock can be shown to bear on the loss and fragmentation of habitats while at the same condemning a large and growing share of all birds and mammals to a short and agonizing existence. What emerges is an indelible image of a pathological mode of production that is violently narrowing how other animals get to inhabit the earth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".