Maturity Level of Rural Leaders in Selected Paddy Farming Technologies in Muda Agricultural Development Authority (MADA) -Malaysia
Bibliographic record
Abstract
To provide good leadership it is necessary for individuals and groups to help bring a rural community to action. As the rural leaders play a function in important programs in agricultural extension. However, The study was conducted to determine the maturity of rural leaders based on maturity model theory towards agricultural technologies In Malaysia Paddy Farming, and explore the relationship between the selected characteristics of the respondents. Data were collected through personal interview from 260 randomly selected in muda agriculture development authority MADA area. A five point Likert scale was used to determine the maturity of rural leaders ranged from 1 = never to 5= always.The majority (63.1%) of the respondents had a moderate level of maturity. The correlation analysis between socio-demographic characteristics and maturity level show that there is a positive and significant relationship between variables age and years of experience in paddy farming, at 0.05 level of significance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".