Assessing Growers’ Perceptions of Effective Extension Methods and Information Communication Technologies for Training Vegetable Growers in Jordan
Bibliographic record
Abstract
The study examined the effectiveness of agricultural extension education methods as perceived by vegetable growers in Jordan to meet their assessed needs in areas of production, marketing and management. A random sample of 98 vegetable growers in two governorates in Jordan was used. Four point Likert-type scales were used as instruments to gather primary data. Cronbach’s alpha coefficients ranged from 0.88 to 0.91 indicated high internal consistency for the scales. Nonparametric methods were used to analyze the data based on approximations to normal distribution. The results showed that aged people with basic education and who are very largely dependent on farm income are still the ones most engaged in vegetable farming. The most preferred extension methods by farmers were farm visit, meeting groups of farmers, result demonstrations and farm tours. Low rated methods include information and communications technologies (ICTs). The method to be chosen depends on the goal and adoption stage, i.e. whether we wish to change knowledge, attitude, skills or behavior. Extension staff needs to be trained on how to use ICT tools as an extension method to enable them to train farmers on how they can use them in extension. The involvement of public extension staff in the survey contributes to the selection of extension delivery method on the bases of its ability to deliver the appropriate information to the targeted farmers in the right time.
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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 | 0.000 |
| 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".