Consumers' Preferences for GM Food and Voluntary Information Access: A Simultaneous Choice Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".