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Record W2031489920 · doi:10.3138/jvme.0411.045r1

A Policy at the University of Adelaide for Student Objections to the Use of Animals in Teaching

2012· article· en· W2031489920 on OpenAlexvenueno aff
Alexandra L. Whittaker, Gail I. Anderson

Bibliographic record

VenueJournal of Veterinary Medical Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAsideLegislatureInstitutionMedical educationProcess (computing)MedicinePsychologyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

In veterinary medical education, the use of animals or cadaveric tissue as a component of teaching practice is common. Teachers are required, during the process of ethical review, to apply the 3 Rs principle (replacement, refinement, reduction) whenever they consider using animals during a teaching exercise. This often involves use of replacement strategies, such as utilization of video footage or simulation-based training. However, aside from legislative or ethical requirements imposed by a country's regulatory framework on the institution, students are often the key advocates for using alternative teaching practices that do not make use of animals. This has prompted many institutions with veterinary and other life sciences teaching programs to develop student-conscientious objection policies to the use of animals in teaching. In this article, we discuss the procedures implemented to make provision for student-conscientious objectors at a new Australian Veterinary School, at the University of Adelaide. We also describe the processes to provide information to students and faculty on this issue and to facilitate information gathering on alternatives.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 head0.525
GPT teacher head0.585
Teacher spread0.060 · 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 designNot applicable
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

Citations11
Published2012
Admission routes1
Has abstractyes

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