MétaCan
Menu
Back to cohort
Record W2166308648 · doi:10.1371/journal.pone.0141878

Public Attitudes to Housing Systems for Pregnant Pigs

2015· article· en· W2166308648 on OpenAlexaffabout
Ellen B. Ryan, D. Fraser, Daniel M. Weary

Bibliographic record

VenuePLoS ONE · 2015
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGestationLikert scaleWelfareOpposition (politics)Animal welfarePsychologyAdvertisingPregnancyBusinessBiologyLawDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.467
GPT teacher head0.356
Teacher spread0.111 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations56
Published2015
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONESame topicAnimal Behavior and Welfare StudiesFrench-language works237,207