Canadian Consumers' Preferences for Food Safety and Agricultural Environmental Safety Research Summary
Notice bibliographique
Résumé
This summary reports on a study of Canadian consumers' attitudes and awareness relative to a variety of food and environmental issues associated with Canadian agriculture. Previous research on consumer attitudes and perceptions of various food safety and environmental issues has included surveys focused on single food technologies or food safety issues. Examples are Govindasamy and Italia (1) on pesticides; Grobe et al. (2) on hormones; Veeman et al. (3) on GM food. Some studies focused on several issues, such as Nayga (4) on irradiation, antibiotics, hormones, and pesticides; Dosman et al. (5) on pesticides, hormones, additives; and Hwang et al. (6) on antibiotics, pesticides, hormones, GM, and irradiation. Gender is concluded to be an important determinant of risk perceptions across a variety of food and environmental concerns. In general, women perceived more risks than men. Dosman et al. found age to be associated with consumers' risk perceptions, suggesting that younger individuals may be more familiar with certain risks, such as risks associated with new technologies and may not have experienced the possible effects of certain health issues and therefore, do not perceive these as risks. Govindasamy and Italia concluded that households with higher levels of income and education exhibit lower risk aversion. Rosati suggested the trustworthiness or reliability of risk is dependent on three determinants: perceptions of knowledge, honesty and concern. There is still research to be done and our work aims to address two key issues: first, what are Canadian consumers' perceptions of food and environment related risks? and second, what underlying factors affect respondents' risk perceptions? Survey and Data: The analysis is based on a Canada-wide survey of 882 participants, drawn from a large representative panel, conducted in January 2003. Eight food safety issues (bacteria contamination, pesticide residues, use of hormones in food additives, use of antibiotics, BSE (mad cow disease), food additives, use of genetic modification/engineering in food production, fat and cholesterol content) and six environmental safety issues (water pollution by chemical run-offs from agriculture, soil erosion through agricultural activity, genetic modification/engineering, resistance to herbicides and pesticides, adverse effects of agriculture on biodiversity, agriculture waste disposal (e.g. animal manure)) were ranked by respondents from 1 (high risk) to 4 (almost no risk) and 5 (don't know). We report and investigate correlations across the levels of concern expressed by individuals for food safety and environmental safety respectively. Models that may explain the levels of concern based on socioeconomic factors that may influence ratings are also assessed Statistical Analysis: In an initial analysis, we normalize each respondent's concern ranking relative to the sets of food safety and environmental safety issues. In this component of the analysis we apply seemingly unrelated (SUR) models to allow for the possibility that a respondent's particular concerns may be influenced by different sets of explanatory variables, while simultaneously allowing for the error term within each set of issues to be correlated. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».