Human Capacity Building in Selected Local Government Areas of Ebonyi State, Nigeria – The Role of Non-Governmental Organizations and Development Agencies (2000 – 2008)
Bibliographic record
Abstract
The role of Non-governmental organizations and Development Agencies in Human Capacity-Building in selected Local Government Areas of Ebonyi State; and the effectiveness of Capacity-building programmes in facilitating community development were examined. The study reviewed the contributions of Scholars in this area and related fields; with particular attention to the activities of indigenous NGOs and the Ebonyi State, Community-Based Poverty Reduction Agency. To effectively evaluate the activities of these bodies, the focus of the Research was on issues relating to human capacity-building through workshops and training as well as issues on capacity-building through micro-projects at the community and council levels. Three hypotheses were formulated and tested using chi-square method. The study adopted the System Approach to training and that of Development participation. Data were collected via: structured questionnaires, interviews, records and documents. The central point of the findings is that the Agencies’ capacity building efforts cannot promote skill acquisition, and most of their outcomes are not sustainable at the grassroot. The study recommended, among others, that the skills acquisition centres should be equipped and that capacity building programmes should involve the people at the critical stages of the process, in addition to sequencing programmes in accordance with assessed needs of the time. That sustainability could be ensured through participative processes to strengthen the involvement of local communities; with special attention to maintenance arrangements. These, would assist the government, NGOs and Development Agencies appreciate new strategies in the efforts on capacity development at the grassroots.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".