Evaluating Community Development Projects Using the OECD Evaluation Criteria
Bibliographic record
Abstract
Background: Community development has over the years become very popular in the development circles. However, efforts and resources expended in improving rural communities through development projects appear to be eroded over time. Failure to integrate evaluation in the project design and periodically assess intended objectives and current results is a major contributory factor. Evaluation has become very significant especially in this era of dwindling donor support. It helps to identify strengths and weaknesses of a project and aids in averting erosion of efforts. This paper practically evaluates community development projects implemented in 4 rural communities in the Eastern region of Ghana using the OECD/DAC five project evaluation criteria which are efficiency, effectiveness, relevance, impact and sustainability. Purpose: The study was conducted to provide a practical example of how community development projects can be evaluated using existing criteria. Also, it is intended to add to existing knowledge on evaluation and encourage project implementers to consider evaluation as an integral part of their implementation. Setting: The study was conducted in four rural communities in two districts in the Eastern region of Ghana. Eastern region is one of the ten regions in Ghana. The projects involved in the study were a School, Clinic, Oil Palm processor, and a Soap and Cosmetics project. Intervention: The study shows practically that evaluation is uncomplicated and can be undertaken in interventions. It outlines clearly the gains and losses that can be generated by community development project. It also outlines the threats and opportunities that exist in the implementation of community development projects. This can be applied in other settings. Research Design: The sample size was 40 made up of 6 NGO staff, 4 local government staff, 2 health assistants and 28 community members including leaders. The community members were sampled randomly while purposive sampling was used for local government, NGO staff and community leaders. Data Collection and Analysis: Data collection included primary methods such as interviews, focus group discussions, photographs, observations, and questionnaire administration as well as secondary methods such as reviews of relevant books, photographs, project reports, policy papers, and relevant websites. Analysis of the data collected was done qualitatively with simple statistical tools. Findings: Some findings of the evaluation were that the school was more relevant, efficient, effective and sustainable. It also had maximum impact. The Oil Palm processor was also more relevant, effective and sustainable. It was however less efficient. The Clinic was less relevant, efficient and effective. It also had less impact on the community. The Clinic was not sustainable. The Soap and Cosmetics project was not relevant, not efficient, not effective not sustainable and did not have any impact on the community.
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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 | 0.079 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".