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Record W2004071319 · doi:10.3138/jvme.1010.130r3

Attitudes of Australian and Turkish Veterinary Faculty toward Animal Welfare

2012· article· en· W2004071319 on OpenAlexvenueno aff
Serdar İzmirli, Clive Phillips

Bibliographic record

VenueJournal of Veterinary Medical Education · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
FundersYükseköğretim KuruluCouncil for Higher Education
KeywordsAnimal welfareTurkishWelfareVeterinary medicineVeterinary educationAnimal-assisted therapyLivestockFocus groupMedical educationMedicinePsychologyPolitical sciencePet therapyPedagogyGeographyMarketingCurriculumBusinessBiology

Abstract

fetched live from OpenAlex

The attitudes of veterinary faculty toward animal welfare were surveyed in four Australian and three Turkish veterinary schools. The former were considered to be typical of modern Western schools, with a faculty of more than 40% women and a primary focus on companion animals, whereas the latter were considered to represent more traditional veterinary teaching establishments, with a faculty of 88% men and a primary focus on livestock. A total of 116 faculty responded to the survey (42 Australian and 74 Turkish faculty members), for response rates of 30% and 33%, respectively. This survey included demographic questions as well as questions about attitudes toward animal-welfare issues. Women were more concerned than men about animal-welfare issues, especially the use of animals in experiments, zoos, entertainment, and sports and for food and clothing. Total scores demonstrated different concerns among Turkish and Australian faculty. The study demonstrates that the veterinary faculty of these two countries have different concerns for animal welfare, concerns that should be acknowledged in considering the welfare attitudes that students may adopt.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.356

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 head0.108
GPT teacher head0.460
Teacher spread0.351 · 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 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

Citations28
Published2012
Admission routes1
Has abstractyes

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