Animal Welfare Science: Recent Publication Trends and Future Research Priorities
Bibliographic record
Abstract
Animal welfare science is a young and thriving field. Over the last two decades, the output of scientific publications on welfare has increased by c. 10-15% annually (tripling as a proportion of all science papers logged by ISI’s Web of Science), with just under half the c. 8500 total being published in the last 4 years. These papers span an incredible 500+ journals, but around three quarters have been in 80 animal science, veterinary, ethology, conservation and specialized welfare publications, and nearly 25% are published in just two: Animal Welfare and Applied Animal Behaviour Science. Farmed animals – especially mammals – have attracted by far the most research. This broadly reflects the vastness of their populations and the degree of public concern they elicit; poultry, however, are under-studied, and farmed fish ever more so: fish have only recently attracted welfare research, and are by far the least studied of all agricultural species, perhaps because of ongoing doubts about their sentience. We predict this farm animal focus will continue in the future, but embracing more farmed fish, reptiles and invertebrates, and placing its findings within broader international contexts such as environmental and food security concerns. Laboratory animals have been consistently well studied, with a shift in recent years away from primates and towards rodents. Pets, the second largest animal sector after farmed animals, have in contrast been little studied considering their huge populations (cats being especially overlooked): we anticipate research on them increasing in the future. Captive wild animals, especially mammals, have attracted a consistent level of welfare research over the last two decades. Given the many thousands of diverse species kept by zoos, this must, and we predict will, increase. Future challenges and opportunities including refining the use of preference tests, stereotypic behaviour, corticosteroid outputs and putative indicators of positive affect, to enable more valid conclusions about welfare; investigating the evolution and functions of affective states; and last but not least, identifying which taxonomic groups and stages of development are actually sentient and so worthy of welfare concern.
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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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".