Reasons Why Vegetable Cultivation Increases or Does not Increase Vegetable Intake among Adult Vegetable Growers Living in a City in Gunma Prefecture, Japan: a Qualitative Study
Bibliographic record
Abstract
We examined the reasons why vegetable cultivation increases or does not increase vegetable intake among adult Japanese vegetable growers. A qualitative cross-sectional study using a self-completed anonymous questionnaire was sent to participants (aged 20–74 years residing in three areas of a city in Gunma Prefecture, Japan) in September 2016. The questionnaire addressed perceptions of whether vegetable cultivation would increase vegetable intake, with four possible answers: strongly disagree, disagree, agree, and strongly agree. Respondents were then asked reasons for their view, with free-text responses. We also asked about participants’ characteristics and whether they found that growing vegetables had changed their vegetable intake and access to vegetables. We categorized the free-text answers by content. We analyzed 442 answers, and reasons for vegetable growing increasing vegetable intake were grouped into five categories: “availability,” “purpose of cultivation,” “quality,” “increased positive emotions toward vegetables,” and “unconsciousness”; for it not increasing intake were also grouped into five categories: “limited quantities,” “negative emotions toward vegetables,” “cultivation for a purpose other than eating vegetables,” “access to vegetables from other sources,” and “limits associated with self-cultivation.”
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".