Community Stakeholder Engagement and Operational Sustainability of Kenya Equity in Education Project in Turkana County
Bibliographic record
Abstract
Operational sustainability of education initiatives in under-resourced and crisis-affected contexts depends heavily on meaningful community stakeholder engagement. This study examined the influence of community stakeholder engagement during project execution on the operational sustainability of the Kenya Equity in Education Project (KEEP) in Turkana County. Grounded in the Ladder Theory of Participation and Stakeholder Theory, the research employed a descriptive design. The sample population comprised 194 respondents, drawn using stratified and simple random sampling techniques to ensure representativeness across key stakeholder groups. Specifically, 11 project staff, 26 teachers, and 154 parents were selected through this approach. In addition, purposive sampling was employed to select three key informants, namely, the Project Manager, the KEEP Education Officer, and the Chairperson of the Parent-Teacher Association, to provide specialised insights into project implementation and sustainability. Data were collected through questionnaires, focus group discussions, and key informant interviews. Questionnaires were administered to parents, teachers, and project staff; Focus Group Discussions were conducted with illiterate parents; and interviews with key informants were conducted. Findings show that while 63.3% of community members contributed labour and 31.6% participated in management committees and received training, there was no community contribution of materials or funds due to widespread poverty. Correlation analysis demonstrated a strong, significant relationship between community stakeholder engagement in project implementation and operational sustainability (r = 0.641; p = 0.000). However, the project's full reliance on donor funding resulted in limited financial and technical sustainability after project completion, as key activities such as scholarships, remedial classes, and facility construction ceased. Managerial sustainability persisted, as trained committees continued to manage existing facilities. The study recommends making community representation in project management and local resource contributions mandatory funding requirements. It also calls for actively involving community members in project planning, building their capacity in resource mobilisation and management, and adopting participatory approaches throughout project implementation to ensure long-term operational sustainability.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".