Analysis of the Use of Local Resources in Extension Education Programme in Nkonkobe Local Municipality of Eastern Cape
Bibliographic record
Abstract
The paper identified the available local resources for extension education and the constraints in the use of these local resources in Nkonkobe local Municipality. The survey was conducted in the peri-urban areas of Fort Beaufort, Alice, Seymour, Balfour, Hogsback and Middledrift from 7th to 29th September 2010 by interviewing 58 farmers on the identification of local resources and their perception of constraints in the use of local resources. The study revealed that there are local resources embedded in the area for use in Extension teaching and learning. The perception of constraint (inadequate access to local resources) increased significantly with age (P = 0.04) and farm experience (P = 0.045). The fundamental strategies for a successful Extension work should be to develop a process which not only creates co-operative platforms for the use of local resources for rural improvement, but also reinforces farmer’s ingenuity and inspires them to learn and accept innovation.The available local resources in Nkonkobe Local Municipality are well distributed in the community and are important for the achievement of educational goals in Extension teaching and learning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".