Integrating On-Farm Animal Welfare Assessments into Regulatory Frameworks: Challenges and Solutions for Improved Animal Care
Bibliographic record
Abstract
Animal welfare is a complex issue of growing importance in global agriculture. This chapter examines the challenges of integrating on-farm animal welfare assessments into Indian regulatory frameworks. It reviews theoretical approaches such as ethology, the Five Freedoms, and ecofeminism, emphasizing animal-based measures like body condition scoring for direct evaluation of animal well-being. These challenges include achieving consistent standards across India’s diverse farming systems, ensuring economic feasibility for farmers, and bridging potential gaps in understanding between farmers and regulators. This chapter advocates for a collaborative approach, involving industry, government, animal welfare science, and consumers, to develop practical, science-based regulatory frameworks tailored to Indian agriculture. Furthermore, technology, including precision livestock farming and sensors, offers promising tools to enhance assessment accuracy, efficiency, and affordability. Drawing on global models from Canada, the EU, and New Zealand, this chapter advocates for integrating on-farm welfare assessments into India’s agricultural regulations, tailored to the specific conditions of smallholder farms and indigenous livestock. This chapter calls for a collaborative and science-based approach to drive continuous improvement in animal care on Indian farms, promoting both animal welfare and agricultural sustainability.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".