The effects of constituency development funds funded projects in contributing to business performance: Asurvey of Juja constituency, Kenya
Bibliographic record
Abstract
The research endeavours to find out the effects of CDF financed projects on micro and small enterprises growth and performance, how construction sector has benefited from the CDF and ways in which CDF administration can be made better in order to give maximum benefits to Juja constituents. This is because the projects like those of increasing security, health facilities ensure that people are able to lead better lives and engage in income generating activities of which businesses flourishing/mushrooming are the clearest. The overall objective of the study was to survey the effects of the Constituency Development Funds financed projects in contributing to business performance in Juja constituency. The target population included business people, suppliers of construction materials in Juja location who were around CDF projects e.g. schools, hospitals etc. that have been funded by CDF. The researcher selected a total of 70 SMEs for interviews. The researcher adopted random sampling procedure. This was done through random sampling by visiting businesses and CDF financed projects in Juja constituency. Qualitative data was purposefully collected by visiting the business people in Juja location. The research instrument used in the study was a questionnaire and an interview schedule with qualitative questions. The major findings were that CDF projects have done little to develop SMEs in Juja constituency. Most of the respondents felt that no visible benefits have been contributed by projects to the growth of small and micro enterprises in Juja. Also local business men said that construction business has not benefited much from CDF projects. Only a few acknowledged some contribution. Majority of the respondents suggested that CDF should offer low interest loans to the local businesses to improve their performance. Management of CDF was suggested by many should be run by professionals and people from within the constituency who understand the local needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".