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Record W3122641405

Risk Perceptions, Social Interactions and the Influence of Information on Social Attitudes to Agricultural Biotechnology

2005· preprint· en· W3122641405 on OpenAlexaboutno aff
Michele M. Veeman, Wiktor Adamowicz, Wuyang Hu

Bibliographic record

VenueRePEc: Research Papers in Economics · 2005
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsProduct (mathematics)MarketingRisk perceptionPerceptionSample (material)Food choiceConsumption (sociology)BusinessGenetically modified foodLatent class modelAgricultureFood safetyPsychologyAdvertisingGenetically modified organismGeographyFood scienceMedicineSociology
DOInot available

Abstract

fetched live from OpenAlex

We assess Canadian’s risk perceptions for genetically modified (GM) food and probe influences of socio-economic, demographic and other factors impinging on these perceptions. An internet-administered questionnaire with two stated choice split-sample experiments that approximate market choices of individual grocery shoppers is applied to elicit purchase behavior from 882 respondents across Canada. Data are collected to assess the influence on respondents’ choices for a specific food product (bread) of 1) product information which varies in content and by source and 2) information provided through labeling. These data also enable: a) analysis of trade-offs made by consumers between possible risks associated with GM ingredients and potential health or environment benefits in food and b) assessment of influences on respondents’ search for/access of product information. We rigorously document the extent and type of variation in Canadian consumers’ attitudes and risk perceptions for a selected GM food. This is pursued in analysis of experiment 1) data using a latent class model to analyze 445 consumers’ choices for bread products. We identify four distinct groups of Canadian consumers: 51% (value seekers) valued additional health or environmental benefits and were indifferent to GM content; traditional consumers (14 %) preferred their normally-purchased food; fringe consumers (4%) valued the health attribute and could defer consumption. Another 32 % (anti-GM) strongly opposed GM ingredients in food irrespective of introduced attributes. Thus there is a dichotomy in Canadian attitudes to GM content in food: a small majority of the sample (55 per cent) perceive little or no risk from GM food, but this is strongly opposed by 46% of respondents. Differences in gender, number of children in the household, education, and age are associated with the likelihood of segment membership. We also report on the search for information on characteristics of the GM food by a sample of 445 respondents with opportunity for voluntary access to related information through hyperlinks in the survey. Slightly less than half actually sought such information. Gender, employment status, rural or urban residency and the number of children in the household all affected the probability that respondents would access information. A further research component examines product choices made in the context of two common GM labeling policies: mandatory and voluntary labeling. We find these two types of strategies to have distinctive impacts on consumers and on measures of social welfare. Knowledge of these may help policy makers to make more informed analyses of the alternative labeling policies. Specific findings also provide base-line measures of Canadians’ attitudes to risks of GM technology in the context of food and environmental risks, as well as documenting the importance of context influences and reference points on consumers’ preferences for GM food. We also develop methodological improvements for accurately estimating the value of information on a negative attribute. The project built upon initial findings from a previous AARI project (#AARI Project #2000D037) and is complemented by research supported through a Genome Prairie GE3LS (Genetics, Ethics, Environment, Economics, Law and Society) project: “Commercialization and society: its policy and strategic implications.”

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.026
GPT teacher head0.307
Teacher spread0.281 · 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

Citations4
Published2005
Admission routes1
Has abstractyes

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