What is a Chicken Worth?
Bibliographic record
Abstract
Abstract “This must constitute, in both magnitude and severity, the single most severe, systematic example of man’s inhumanity to another sentient animal.”1 So said John Webster, emeritus professor of animal husbandry at the University of Bristol’s Veterinary School. He was referring, rather surprisingly, to broiler chickens. In reaching this conclusion, he noted that in the United Kingdom alone, one-quarter of the heavy strains of these animals are in chronic pain for at least one-third of their six-week lives; only 10% are able to walk normally; up to 6% die during rearing; 4% have chronic arthritis; 3% break their bones; and 2 million die during transport each year. In 2003, some 9.1 billion of these animals were eaten in the United States,2 3.5 billion in the European Union, and 840 million in the United Kingdom.4 In less than 50 years, the broiler chicken—so named because of the way it is cooked— has become one of the most common animals consumed in human diets. The poultry meat sector is a significant employer (40,000 to 50,000 jobs in the United Kingdom alone) as well as a major consumer of cereals, soy, and meat, bone, and fish meals. These birds are reared intensively, in large numbers and at relatively low cost, and they provide a ready source of palatable tender meat for many people.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".