Food Integrity and Food Technology Concerns in Canada: Evidence from Two Public Surveys
Bibliographic record
Abstract
Food integrity and food technologies both generate public concerns. There is little research to show the interactions between those concerns in particular samples, especially in Canada. In this paper, data from two national online samples are used to examine an aggregate of food integrity concerns, genetic modification in food, and food nanotechnology concerns in the Canadian public. A variety of trust, health, environmental, and science attitude variables are used to help explain the concerns that vary across the population. In addition, the food integrity concerns are tested as explanatory variables in the technology concern models to establish whether there is a strong or weak link between the two. Tobit and ordered probit regressions are used to model the variables for each of the survey samples. Results are examined to see if they are consistent across surveys and also consistent with an earlier study that was done in Australia. The results suggest that trust in people and trust in a variety of agents within the food system are beliefs that ameliorate concerns about food integrity and the two technologies. However, trust in advocacy organizations appears to be related to higher concerns in each case. Fundamentally and similar to the earlier Australian study, positive scientific attitudes are a major determinant of reduced concerns about food integrity and the two technologies.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".