Agricultural Biotechnology: A Comparison of Consumers' Preferences for Selected Policy Options
Bibliographic record
Abstract
This paper reports on a study of consumers' preferences for regulatory policies that relate to food biotechnology. Data on consumers' choices of selected policy options were collected through a telephone survey of Alberta residents conducted in early 2000. Conditional and mixed logit models were developed and tested. These assess the influence of different socio‐economic factors on respondents' choices of particular policy options and are used to estimate respondents' willingness to pay for two policy options that were the major focus of the study, specifically: a food labeling system that gives more information about agricultural biotechnology for food more food inspection. In the course of the telephone interviews, consumers were asked to choose between one of these policy options and the option of more restrictive regulation for agricultural biotechnology. The results of the analysis suggest that the sampled consumers tend to most prefer and be most willing to pay for the information provided by a food labeling policy; there is a preference for a labeling policy that will give more information over a policy of more restrictive regulation. Similarly, a policy that will increase food inspection tends to be preferred to more restrictive regulation. There are gender differences in preferences, with females being willing to pay more than males for both the food policy options of labeling and inspection relative to agricultural biotechnology. L'article expose une étude sur les préférences des consommateurs à l'égard des politiques visant à réglementer la biotechnologie dans l'agroalimentaire. Des données sur ces préférences ont été recueillie dans le cadre d'un sondage téléphonique auprès des habitants de l'Alberta, au début de 2000. On a ensuite élaboré et testé des modèles à logit conditionnel et à logits mixtes pour évaluer l'influence de divers paramètres socio‐économiques sur le choix d'une politique particulière par les répondants. Les mêmes modèles ont servi à estimer dans quelle mesure les répondants étaient prêt à payer pour une des deux options examinées, à savoir i) un système d'étiquetage des produits alimentaires les renseignant davantage sur l'usage de la biotechnologie en agriculture et ii) des inspections plus nombreuses pour les produits alimentaires… Durant l'entrevue, on a demandé aux consommateurs de choisir entre une de ces deux options et une réglementation plus sévère du recours à la biotechnologie en agriculture. Les résultats de l'analyse laissent croire que la plupart des consommateurs préfèrent et serait plus enclins à payer l'information obtenue grâce à une politique d'étiquetage; on privilégie un système d'étiquetage qui fournira plus de renseignements à une réglementation coercitive. De même, on préfère des inspections plus nombreuses à une réglementation plus sévère. Les préférences varient avec le sexe, les femmes acceptant plus que les hommes de payer davantage pour les deux options proposées (étiquetage et inspections) vis‐à‐vis de la biotechnologie.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".