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Do environmental attitudes and food technology neophobia affect perceptions of the benefits of nanotechnology?

2012· article· en· W1920433742 on OpenAlexafffundabout
Anahita Hosseini Matin, Ellen Goddard, Frédéric Vandermoere, Sandrine Blanchemanche, Andréa Bieberstein, Stéphan Marette, Jutta Roosen

Bibliographic record

VenueInternational Journal of Consumer Studies · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsNeophobiaPerceptionAffect (linguistics)Food technologyFood industryPurchasingFood processingMarketingPsychologyNanotechnologyBusinessPolitical scienceMaterials scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract In recent years, a significant amount of research has focussed on the analysis of consumers' aversion to new technologies in food production and processing. At the same time, research has shown that environmental attitudes may be related to purchasing behaviour of consumers. This paper presents the result of an investigation into Canadian attitudes towards nanotechnology, in general, and in applications in the food industry. The relationship between the food technology neophobia scale, environmental attitudes and nanotechnology is examined. The results suggest that food technology neophobia is significant in explaining attitudes towards nanotechnology, in general, and for food packaging and foods. Environmental attitudes are important in explaining respondents' attitudes towards nanotechnology in general but not in explaining attitudes towards nanotechnology in food packaging or food applications. Survey respondents' views of the role of science and technology in society (makes society worse or better off) are a more important determinant of attitudes towards nanotechnology than whether they had heard of nanotechnology prior to the survey.

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)0.000
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 score0.010
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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

Citations98
Published2012
Admission routes3
Has abstractyes

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