Canadian Consumers' Preferences for Food Safety and Agricultural Environmental Safety Research Summary
Bibliographic record
Abstract
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. …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".