Animal welfare assurance programs in food production: a framework for assessing the options
Bibliographic record
Abstract
Abstract Various animal welfare assurance programs are being used to encourage or require the adoption of animal welfare standards in food production, and to assure the public that such standards are followed. The programs involve five main formats. Non-mandatory codes/guidelines are relatively easy to institute and appear well-supported by the industry, but provide only minimal assurance to the public unless measures are taken to ensure compliance. Programs based on government regulations and inter-governmental agreements are more challenging to institute; they are likely to generate less industry acceptance, but may provide more public confidence if enforcement is adequate. Product differentiation programs, and retailer policies requiring products to meet certain standards, serve a range of functions; these may generate public confidence but only for products covered. The various programs include several types of requirements. Requirements that are designed to maintain animal health and functioning have a widely accepted scientific basis, are often easy to incorporate into existing production systems, and often provide economic benefits, but do not fully address public concerns over animal welfare in some cultures. Requirements that address pain, distress and other affective states, and those that accommodate certain natural behaviour, have a growing but less traditional scientific rationale and appear likely to generate public confidence; however, they sometimes require significant changes to existing practices. Requirements for more natural surroundings (outdoor, free-range) seem to generate public confidence, but appear most likely to increase costs, least likely to be supported by the existing industry, and may involve trade-offs with productivity and with other aspects of animal welfare. The various formats and requirements provide a range of policy options for addressing animal welfare concerns in different cultural, industry and market contexts.
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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 | 0.140 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.041 | 0.016 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".