MétaCan
Menu
Back to cohort
Record W2110560640 · doi:10.1177/0963662510362834

Attitudes towards the use of genetically modified animals in research

2010· article· en· W2110560640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePublic Understanding of Science · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaGenome British ColumbiaGenome Canada
KeywordsGenetically modified organismDemographicsAgricultureGenetically modified maizeBiotechnologyBiologyGenetically modified cropsSociologyDemographyTransgene

Abstract

fetched live from OpenAlex

Here we provide the first experimental evidence that public concerns about the use of animals in research are accentuated when genetically modified (GM) animals are used. Using an online survey, we probed participant views on two uses of pigs as research animals (to reduce agricultural pollution or to improve organ transplant success in humans) with and without GM. We surveyed 327 animal technicians, researchers, advocates, university students and others. In both scenarios and across demographics, support dropped off when the research required the use of GM pigs or GM corn. For example, 66% of participants supported using pigs to reduce phosphorus pollution, but this declined to 49% when the pigs were fed GM corn and to 20% when the research required the creation of a new GM line of pigs. Those involved in animal research were more consistently supportive compared to those who were not or those who were vegetarians.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.338
GPT teacher head0.429
Teacher spread0.091 · 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