Predictors of Youth Voluntary Participation in Urban Agriculture Programme in Malaysia: A Review
Bibliographic record
Abstract
Urban agriculture (UA) has drawn the attention of Malaysian policy makers. University Putra Malaysia (UPM) has taken the lead in introducing programmes to urban residents. The university’s strategy is to train student volunteers to play the role of change agents serving urban residents to implement the programme. UPM programme planners need to build a large population of long-term students who voluntarily participate in the programme. Hence the university, specifically the faculty of agriculture, is meeting the challenge to produce candidates prepared with the knowledge, skills, and disposition to participate voluntarily in the UA programme. This paper reviews the existing literature on factors to predict voluntary participation among young students. The authors propose a conceptual model for programme developers to promote youth participation in a voluntary programme. Research is recommended to predict factors influencing UPM students’ voluntary participation in the UA programme. Further research is also recommended to explore how programme planners can overcome potential barriers to students’ participation in the programme. These investigations could help stakeholders design a programme that appeals to more students and urban residents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".