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Record W1979143953 · doi:10.3138/jvme.36.3.276

An Approach to Teaching Animal Welfare Issues at The Ohio State University

2009· article· en· W1979143953 on OpenAlexvenueno aff
Linda K. Lord, Jennifer Walker

Bibliographic record

VenueJournal of Veterinary Medical Education · 2009
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareState (computer science)Medical educationMedicineEngineering ethicsEngineeringBiologyComputer scienceEcology

Abstract

fetched live from OpenAlex

Despite the growing importance of animal welfare and the critical role of the veterinary profession, animal welfare is not formally taught in many veterinary curricula. In addition, veterinary students are often not exposed to current contentious animal welfare issues, which are subject to much debate and often proposed regulation. To address this deficiency in our curriculum at The Ohio State University College of Veterinary Medicine, we have developed a course titled "Contemporary Issues in Animal Welfare." Our specific objectives for the course are: 1) to provide students with the opportunity to objectively evaluate and discuss current issues in the welfare of animals as companions, and in the industries of agriculture, science, education, conservation, and entertainment; 2) to increase students' awareness of current important animal welfare issues; and 3) to develop students' skills in the critical evaluation of written and visual material used in the scientific literature and lay press. We hope that, over time, this teaching model will be considered a means to educate veterinary students about animal welfare issues in other veterinary curricula.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.068
GPT teacher head0.392
Teacher spread0.323 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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