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Record W3124339214

A Comparative Analysis of Canadian Consumers’ WTP for Novel Food Technologies (Case of Juice Produced by Nanotechnology & Pork Chops Using Genomic Information)

2013· article· en· W3124339214 on OpenAlexaboutno aff
Anahita Hosseini Matin, Ellen Goddard

Bibliographic record

Venue2013 Annual Meeting, August 4-6, 2013, Washington, D.C. · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEmerging technologiesMarketingGenomic informationConsumer demandBiotechnologyEconomicsGenomeNanotechnologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Since novel food technologies (such as nanotechnology, cloning, genomics, etc.) are still in their infancy, communication will be very important in the development of these new technologies to address consumer perceptions and hence market acceptance of these innovations in the agri-food industry. Understanding consumer preferences is key to ensuring that the use of new technologies optimizes use of resources and societal welfare. Two national online surveys (in 2010 for nanotechnology and in 2012 for genomic information) were conducted across Canada to elicit Canadian consumers’ WTP for juice produced by nanotechnology or pork chops that are produced from pigs bred using genomic information. Canadian consumers’ WTP (i.e. whether or not they are willing to buy the products at a price over the price of goods produced without the use of the technologies), and the effects of demographic characteristics, Canadian consumers’ attitudes on their purchase intentions about products created using these novel technologies, were examined. The preliminary analysis shows that the majority of Canadians have little knowledge about use of genomic information or nanotechnology, and hence are not willing to pay a premium for these novel technologies applied to their food.

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)0.001
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 score0.179
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.258
Teacher spread0.228 · 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 designBench or experimental
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

Citations1
Published2013
Admission routes1
Has abstractyes

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Same venue2013 Annual Meeting, August 4-6, 2013, Washington, D.C.Same topicGenetically Modified Organisms ResearchFrench-language works237,207