Public Attitudes to Housing Systems for Pregnant Pigs
Bibliographic record
Abstract
Understanding concerns about the welfare of farm animals is important for the development of socially sustainable production practices. This study used an online survey to test how views on group versus stall housing for pregnant sows varied when Canadian and US participants were provided information about these systems, including access to scientific papers, YouTube videos, Google images, and a frequently-asked-questions page (S1 Appendix). Initial responses and changes in responses after accessing the information were analyzed from Likert scores of 242 participants and from their written comments. Participants were less willing to accept the use of gestation stalls after viewing information on sow housing. For example, initially 30.4% of respondents indicated that they supported the use of gestation stalls; this declined to 17.8% after participants were provided additional information. Qualitative analysis of comments showed that supporters of gestation stalls expressed concern about the spread of disease and aggression between animals in less confined systems, whereas supporters of group housing placed more emphasis on the sow's ability to interact socially and perform natural behaviors. These results point to public opposition to the use of gestation stalls, and indicate that the more that the public learns about gestation stalls the less willing they will be to accept their use.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".