MétaCan
Menu
Back to cohort
Record W2942139446 · doi:10.1163/15685306-12341640

Shifting Preservice Teachers’ Views of Animal Welfare and Advocacy through a Human-Animal Relationships Course

2019· article· en· W2942139446 on OpenAlexaff
Christine Yvette Tardif-Williams, John-Tyler Binfet, Camille X. Rousseau

Bibliographic record

VenueSociety and Animals · 2019
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaBrock University
Fundersnot available
KeywordsSummative assessmentCurriculumAnimal welfareFeelingPsychologyPedagogyAnimal-assisted therapyLiteracyWelfareMedical educationFormative assessmentPolitical sciencePet therapySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Abstract This mixed-methods study explored how participation in an intensive course on human-animal relationships impacted preservice teachers’ views about human-animal welfare and advocacy and animal-focused curriculum. Participants were 25 undergraduate students (24 female; 1 male) following a teacher education pathway. Participants completed the Animal Rights Scale , and their insights on assigned readings were captured through weekly journal entries and responses to summative prompts. Participants reported feeling increased responsibility to advocate on behalf of nonhuman animals and greater support of animal welfare during the post-course (versus pre-course) assessment, and participants’ weekly and summative responses revealed some of the nuances and internal tensions in their thinking about integrating animal-focused curriculum as part of their future professional practice. As teachers play key roles fostering humane literacy and engaging young people with actual nature and animals, these findings have implications for both education and higher education curriculum and initiatives.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.005
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.083
GPT teacher head0.365
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueSociety and AnimalsSame topicAnimal and Plant Science EducationFrench-language works237,207