Replication data for: How should the public contribute to discussions on cattle welfare? Perspectives of veterinarians and animal scientists
Bibliographic record
Abstract
Veterinarians and animal scientists can provide leadership on issues relevant to farm animal welfare, but perceptions of these stakeholders regarding societal expectations for welfare are underexplored. This study involved five focus groups of veterinarians and animal scientists (n=50 in total), recruited at a European meeting focused on cattle welfare. Participants were invited to discuss topics related to cattle welfare and were prompted with questions designed to elicit their perspectives of public concerns and how public input should be included when developing solutions. Discussions were moderated by trained facilitators, audio-recorded and transcribed, and transcripts coded through reflexive thematic analysis. Ultimately four primary themes were developed: 1) The public as concerned; 2) The public as ignorant; 3) The public as needing education; and 4) The public as helper or hindrance. Groups identified specific practices viewed as concerning to the public, including lack of pasture access, behavioural restriction, and painful procedures. Discussions about these concerns and the role of the public were often framed around the assumption that the public was ignorant about farming, and that this ignorance should be rectified through education. Participants were generally ambivalent about how and if the public should contribute to discussions on farm animal welfare, but suggested that consumers should pay more for products to help shoulder any costs of welfare improvements.
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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.122 | 0.043 |
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".