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Record W2582213674 · doi:10.22434/ifamr2016.0177

Factors affecting cattle producers’ willingness to adopt an Escherichia coli O157:H7 vaccine: a probit analysis

2017· article· en· W2582213674 on OpenAlexafffundabout
Brian J. Ochieng’, Jill E. Hobbs

Bibliographic record

VenueThe International Food and Agribusiness Management Review · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Agriculture - Saskatchewan
KeywordsBusinessIncentiveFood safetyOrdered probitPsychological interventionMultivariate probit modelMarketingAgricultural scienceBiotechnologyPublic economicsEconomicsFood scienceBiology

Abstract

fetched live from OpenAlex

E. coli O157:H7 bacteria – a major cause of foodborne illness – occur naturally in the intestine of cattle but do not affect the health or productivity of the animal. A cattle vaccine that significantly reduces the risk of E. coli contamination was developed and commercialized in Canada and internationally, however, adoption by cattle producers remained extremely low. Utilizing data from a survey of cow-calf producers in western Canada, this paper examines the factors affecting cattle producers’ willingness to adopt the E. coli vaccine. Education, prior awareness of the vaccine, perception of who holds primary responsibility for E. coli risk reduction, and a producer’s external (versus internal) locus of control with respect to their ability to mitigate E. coli risks within the production environment are significant determinants of willingness to adopt. Adoption incentives are also evaluated, including policy interventions, market/supply chain incentives, production protocol, and producer reputation incentives. The analysis provides lessons for the development and commercialization of vaccines and other food safety intervention strategies that yield societal and supply chain benefits beyond the individual adopter.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.416
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.059
GPT teacher head0.296
Teacher spread0.236 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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