Monitoring and Evaluation Work Plan and Sustainable Water Supply in Nyamira South Sub-County, Kenya
Bibliographic record
Abstract
This study assessed the influence of Monitoring and Evaluation (M&E) work plan on sustainable water supply in Nyamira South Sub-County, Kenya. The study was based on a theoretical framework composed of monitoring and evaluation work plan of water supply as the independent variable and sustainable water supply as the dependent variable. Frequencies, percentages, mean, and standard deviation; simple and multiple linear regression and Pearson Correlation Coefficient models were applied on the quantitative data while narrative analysis was applied on the qualitative data. The total respondents who agreed and strongly agreed that the water supply in Nyamira South Sub-County was: long enough were 22.7 per cent; adequate for the needs of the people were 22.5 per cent; of good quality were 31.2 per cent; safe for drinking were 32.1 per cent. Those who agreed that funds generated from the water supply were adequate were 15.4 per cent; and that the water was affordable were 22.1 per cent. The qualitative findings suggested that the water supply was not sustainable. Based on the composite mean and standard deviation of 2.47 and 1.01 respectively, the study suggested that the M&E work plan for Nyamira South Sub-County water supply existed and was available. However, the work plan was not in use, was not regularly reviewed nor updated. The study’s hypothesis of no significant relationship between M&E work plan and sustainable water supply in Nyamira South Sub-County, Kenya was rejected since P=0.000<0.05. Strengthening the use of M&E work plan in project implementation was recommended.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".