Transforming Intensive Animal Production: Challenges and Opportunities for Farm Animal Welfare in the European Union
Bibliographic record
Abstract
Since the 1960s, the European Union (EU) has made efforts to ensure the welfare of farm animals. The system of EU minimum standards has contributed to improved conditions; however, it has not been able to address the deeper factors that lead to the intensification of animal farming and the consolidation of the processing sector. These issues, along with major competitive pressures and imbalances in economic power, have led to a conflict of interest between animal industries, reformers, and regulators. While the priorities of the European Green Deal and the End the Cage Age initiatives are to induce a rapid phasing out of large-scale cage-based farming systems, the industry faces the need to operate on a highly competitive global market. Animal farmers are also under pressure to decrease input costs, severely limiting their ability to put positive animal-care values into practice. To ensure a truly effective transition, efforts need to go beyond new regulations on farm animal welfare and address drivers that push production toward a level of confinement and cost-cutting. Given the right socio-economic and policy incentives, a transition away from intensive farming methods could be facilitated by incentives supporting farm diversification, alternative technologies, and marketing strategies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".