Animal Welfare: A Complex Domestic and International Public-Policy Issue—Who Are the Key Players?
Bibliographic record
Abstract
Animal-welfare issues are usually portrayed in the media in a black-and-white fashion, with simple, single-perspective solutions proposed for what are often, in fact, complex policy issues. In this article, we argue that animal welfare is a multifaceted international and domestic public-policy issue that must take account of not only scientific, ethical, and economic issues but also religious, cultural, and international trade policy considerations. Management of animal welfare at a government policy level also requires an approach based on incremental change. Such change must be both science based and ethically principled, and the rate of change must recognize both the expectations of society and the constraints on the animal user. Ideally, such change should involve full ownership and buy-in from the affected animal user group. The range of stakeholders involved in the animal-welfare debate includes industry and producer groups, science bodies, and animal-welfare non-governmental organizations and professional groups, including the veterinary and legal professions. The veterinary profession, in particular, is expected to play an animal-welfare leadership role, and we discuss expectation versus reality at both a national and an international level. This latter discussion includes specific reference to the role of the World Organisation for Animal Health (the OIE) as an intergovernmental organization representing 175 countries and details some of the major achievements since the OIE assumed its international animal-welfare standard-setting role in 2002. We also address the role of the veterinary profession at national, regional, and international levels.
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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 | 0.043 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.029 | 0.030 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".