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Record W2466537026 · doi:10.2527/af.2015-0027

Turning science into policy: The case of farm animal welfare in Canada

2015· article· en· W2466537026 on OpenAlexaffabout
David Fraser

Bibliographic record

VenueWBI Studies Repository · 2015
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnimal welfareWelfarePolitical scienceEnvironmental ethicsPublic economicsPublic administrationBiologyEconomicsEcologyLawPhilosophy

Abstract

fetched live from OpenAlex

Implications Development of farm animal welfare standards in Canada has evolved significantly over 35 yr in terms of process, leadership, and the role of science. Key elements of the current process include: 1) influential producers and producer organizations that see the benefit of having science-informed standards, 2) a credible coordinating body to ensure that a well-defined process is followed in developing standards, and 3) trusted scientists who are engaged in relevant research and willing to participate. The process benefits from having a distinct and defined role for the scientists, specifically to analyze relevant science and identify conclusions that are scientifically justified. Active participation of the retail sector may prove important for ensuring compliance.

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)0.015
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.760

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.026
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0390.018
Scholarly communication0.0170.004
Open science0.0040.007
Research integrity0.0130.011
Insufficient payload (model declined to judge)0.0080.000

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 head0.058
GPT teacher head0.356
Teacher spread0.298 · 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.

Study designQualitative
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
Published2015
Admission routes2
Has abstractyes

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