The Relationship between Farming Experiences and Attitudes Toward Locally Grown Foods Among Japanese Children
Bibliographic record
Abstract
In Japan, introducing farming experiences in the context of school has become popular in promoting “locally produced, locally consumed” foods. This study examined the relationship between farming experience and “attitudes toward locally grown foods” and “attachment to region” among Japanese children. In total, 1464 fifth-grade children in Japan participated in this study and completed questionnaires on their farming experiences, attitudes toward locally grown foods, and attachment to the region in which they live. The scales concerning “attitudes toward locally grown foods” and “attachment to region” were scored, and the scores were compared according to whether the child had farming experience using the Kruskal–Wallis test. About one-quarter of the children (25.6%) responded that they “very often” had farming experiences, and the scores for “attitudes toward locally grown foods” and “attachment to the region” were highest among the children who answered that they had experienced farming “very often” (both P < 0.001). Additionally, significant positive relationships between farming experience and “attitudes toward locally grown foods” (partial correlation coefficient r = 0.171, P < 0.001) and “attachment to region” ( r = 0.156, P < 0.012) were found, even after adjusting for demographic characteristics. The results suggest that having the opportunity to experience farming was associated with more positive attitudes toward locally grown foods and the sense of attachment to one's region among children.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".