Providing appropriate information to consumers boosts the acceptability of genome-edited foods in Japan
Bibliographic record
Abstract
The Japanese Health Ministry recently granted permission for the market distribution of genome-edited (GE) foods, yet there remains a lack of full understanding among consumers regarding this technology. In this study, we conducted a survey to assess the acceptability of GE foods among Japanese consumers and examined the impact of providing information about GE foods on their acceptability. We conducted a web-based survey among 3,408 consumers aged 20-69 years, focusing on three aspects: (1) the commercial availability of GE foods, (2) the consumption of GE foods by others, and (3) your own consumption of GE foods. The survey findings revealed that participants were most accepting of the consumption of GE foods by others, followed by their acceptance of GE foods being commercially available. Notably, participants' acceptance of GE foods increased in all three aspects after they viewed an informative video. The video had a particularly strong impact on participants who fully or partially understood its content, compared to those who did not. Furthermore, regression analyses showed that participants' understanding of two key areas, namely "Why are GE foods important" and "What procedures are in place to ensure the safety of GE foods," played a crucial role in increasing acceptability. Overall, these results indicate that providing information about GE foods to Japanese consumers can effectively enhance their acceptance of such foods. The findings highlight the importance of understanding the benefits and safety measures associated with GE foods in influencing consumer attitudes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".