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Record W4386743874 · doi:10.3390/ani13182907

Crucial but Neglected: Limited Availability of Animal Welfare Courses in Education of Wildlife Researchers

2023· article· en· W4386743874 on OpenAlexaboutno aff
Miriam A. Zemanova

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWildlifeAnimal welfareBachelorWelfareCurriculumEngineering ethicsPolitical scienceEnvironmental ethicsMedical educationPsychologyMedicineEcologyEngineeringBiologyPedagogy

Abstract

fetched live from OpenAlex

Animal welfare is a subject of increasing scientific and ethical concern in today's society, crucial for the well-being of animals used in research and the integrity of scientific data. Equipping researchers in the life science disciplines with a science-based knowledge of animal welfare, behaviour, physiology, and health is, therefore, essential. Nevertheless, previous studies evaluating animal welfare education focused on veterinary, laboratory, or farm animal science. Consequently, the aim of this study was, for the very first time, to map the prevalence of animal welfare courses in the university education of ecologists, wildlife biologists, and conservation managers in Europe, Canada, the USA, Australia, and New Zealand. A comprehensive assessment of 1548 universities was conducted, resulting in the identification of 596 relevant programs at the bachelor's and master's levels. Analysis of the curricula revealed that only 1% of the programs offered a formal course on animal welfare, while 65% provided courses on animal behaviour, 59% on animal physiology, and 34% on animal health. However, the majority of these courses were listed as electives rather than mandatory components of the programs. These results underscore the need for universities to incorporate more formal and obligatory education in animal welfare in order to better prepare future ecologists, wildlife biologists, and conservation managers for the challenges of working with wildlife.

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)0.001
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 score0.385
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.127
GPT teacher head0.402
Teacher spread0.275 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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