Bibliographic record
Abstract
1. Most people in the developed world agree on what 'animal welfare' is, although it is impossible to give it a precise scientific definition. 2. The argument is made that animal welfare is all to do with the feelings of animals and not the primary needs that these feeling have evolved to protect. 3. Acceptance of subjective feelings as a legitimate subject for scientific investigation has a long and well-established history in science. This acceptance was interrupted by the rise of Behaviorism in the 20th century, but now seems to be re-established. 4. Subjective feelings cannot be studied directly. However, in the animal welfare debate, indirect evidence on feelings is extremely useful, and methods for obtaining this indirect evidence are described. 5. The poultry species are capable of feeling several states of suffering including fear, frustration and pain. A start has been made to elucidate these states and the conditions that cause them, but much remains to be done. Recent evidence suggests that the poultry species may also be capable of experiencing pleasure. 6. It is concluded that, although poultry welfare is all to do with the subjective feelings of the birds, it is possible to be objective and scientific about these feelings. Investigation into poultry welfare, therefore, really is science rather than subjectivity.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.040 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".