Towards a Professionalism of Agricultural Extension: Key to Promoting Extension Effectiveness in Africa
Bibliographic record
Abstract
A survey of a small sample of 123 extension workers in Ghana and Ethiopia may have revealed the key issue facing extension ineffectiveness in Africa—the paucity of extension training. Questions on job satisfaction showed that respondents were highly satisfied with: a) an opportunity to work in an area they are trained (N=88, 57%), b) level of education (N=75 (61%), c) enthusiasm towards their work (N=72 (58.5%), and d) opportunity to work with local farmers. However, they were highly dissatisfied with: a) their salaries (N=108 (87.8%), b) the process of decentralization (N=83 (75.6%), c) cooperation from non-governmental agencies (N=87 (70.7%), d) respect from peers in other sectors of government (N=83 (68.0%), e) training in development (N=79 (65.3%), and f) training in communication (N=65 (54.2%). We find their complaints on lack of or limited training in development and communication worthy to note because in the last 40 years extension performance has stressed facilitation among development partners. It can be argued, therefore, that the slow process of decentralization, the friction between extension workers and their NGO counterparts, and their inability to get along with their compatriots in other ministries can be traced to their limited or lack of training in development and communication, otherwise called “Communication for Development” (C4D). We conclude, therefore, that if extension workers are to be effective in their new role as development facilitators not only must their training in agriculture expand to include C4D but, equally significant, extension systems throughout the continent must operate as a profession. Therefore, the authors recommend the establishment of a professional association throughout Africa, under the rubric of “ExtensionAfrica,” which will address the need for extension effectiveness on the continent. We particularly recommend a collaborative relationship with the African Forum for Agricultural Services (AFAAS), which has a mandate for advising on extension services, to make this happen.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".