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Record W2777952995 · doi:10.3390/ani7120102

An Exploration of Industry Expert Perception of Equine Welfare Using Vignettes

2017· article· en· W2777952995 on OpenAlexaff
Helen Hambly, Derek B. Haley, Katrina Merkies

Bibliographic record

VenueAnimals · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVignetteWelfarePerceptionPsychologyIgnoranceSocial psychologyIntervention (counseling)Ranking (information retrieval)Diversity (politics)Applied psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

= 14) were presented with twelve short scenarios in which a horse's welfare could be compromised. They were asked to rank each scenario (with 0 indicating no welfare concerns and 5 indicating a situation where immediate intervention was necessary), provide justification for their ranking, and give examples of what might have been the motivation behind the scenario. The wide range within vignette scores demonstrated the diversity of opinion even among a relatively small group of equine professionals. Qualitative analysis of responses to vignettes suggested that respondents typically ranked situations higher if they had a longer duration and the potential for greater or longer-lasting consequences (e.g., serious injury). Respondents were also the most sensitive to situations in which the horse's physical well-being (e.g., painful experience) was, or could be, compromised. Financial reasons, ignorance, and human convenience were also areas discussed as potential motivators by survey respondents. Overall, responses from the vignettes allowed for a picture of welfare perception based on personal values.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.397
GPT teacher head0.506
Teacher spread0.108 · 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.

Study designBench or experimental
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

Citations7
Published2017
Admission routes1
Has abstractyes

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