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Consumers' Preferences for GM Food and Voluntary Information Access: A Simultaneous Choice Analysis

2009· article· en· W2114945601 on OpenAlexafffundvenue
Wuyang Hu, Wiktor Adamowicz, Michele M. Veeman

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Alberta
FundersGenome AlbertaGenome Canada
KeywordsHumanitiesPsychologyCombinatoricsMathematicsPhilosophy

Abstract

fetched live from OpenAlex

Previous studies on information and consumer choices have typically assumed that information is exogenous in that information made available to consumers is generally treated as being both received and processed. Based on a choice experiment on consumers' stated preferences for genetically modified food that provides for voluntary information access, this study allows information access decisions to be endogenous to the product choice decision‐making process. Instead of assuming correlated error terms between these decisions, the approach used in this analysis builds upon structural correlations between two models that individually consider each of these two decisions. We find that the two types of decisions are related and that there is heterogeneity across individuals in the nature of this relationship. Des études antérieures sur l'information et les choix des consommateurs ont typiquement supposé que l'information est exogène, en ce sens que l'information mise à la disposition des consommateurs est généralement considérée comme étant à la fois reçue et traitée. Selon une méthode de choix expérimentaux sur les préférences déclarées des consommateurs quant aux aliments génétiquement modifiés offrant l'accès volontaire à l'information, la présente étude permet aux décisions d'accès à l'information d'être endogènes au processus de décision lié au choix du produit. Au lieu de supposer des termes d'erreur corrélés entre ces décisions, la méthode utilisée dans la présente analyse mise sur la corrélation structurale entre les deux modèles qui examinent individuellement ces deux décisions. Nous trouvons que les deux types de décisions sont liés et qu'il existe une hétérogénéité entre les individus quant à la nature de ce lien.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.056
GPT teacher head0.182
Teacher spread0.127 · 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.

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

Citations17
Published2009
Admission routes3
Has abstractyes

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