Knowledge Sharing between Farmer Field School Graduate Farmers and Other Farmers on Improved Cocoa Cultivation Practices in Edo State, Nigeria
Bibliographic record
Abstract
<p>This paper investigated the extent of knowledge sharing by cocoa FFS graduates farmers in Edo State with other cocoa farmers. The objectives of the study included to: ascertain the extent of knowledge sharing by FFS farmers, the nature of knowledge shared and the number of beneficiaries from the shared knowledge. A multistage sampling procedure was used to collect data from 68 respondents. A well structured questionnaire was used for data collection. Simple descriptive statistics (frequency counts and percentages) and logit regression were used to analyze the data. The results of the study showed that there was no significant sharing of knowledge by the FFS farmers with other farmers as only 13(19.1%) FFS farmers were involved in knowledge sharing. The logit regression result showed that all the socio-economic variables except household size and farm size were insignificant in influencing the FFS farmers’ knowledge sharing abilities. From the findings of the study, it was therefore recommended that FFS graduate farmers should be encouraged to sign knowledge sharing contract, to organize field day and the need for FFS facilitators to monitor the graduates to ensure that the contractual agreement is adhered to should be stressed.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".