Legal Frameworks, Political Environment and Performance of Biosocial Projects in Informal Settlements in Nairobi County, Kenya
Bibliographic record
Abstract
This scholarly work studied Legal frameworks, political environment and performance of biosocial projects in informal settlements in Nairobi County, Kenya. Purpose of this scholarly work was to ascertain political environment moderate’s relationship between legal frameworks and performance of biosocial projects in informal settlements in Nairobi County, Kenya. The variable indicators were derived from legal frameworks and political environment indicators as independent variables against performance of biosocial projects indicators as dependent variable of this scholarly work. The study was premised on project theory for the two independent variables and for the dependent variable theory of constraint. In this study Pragmatism and mixed research approach were embraced to examine political environment, legal frameworks and performance of biosocial projects while descriptive and correlational research designs were adopted. Self dispensed questionnaires were administered to gather quantitative data while interview guides were used to collect qualitative data after the pilot testing of research instruments to test validity through content related method and reliability through test-retest criterion. A sample size of 183 individuals from 61 biosocial projects were selected from a target sample of 70 biosocial projects in Nairobi County through Gakuu, Kidombo and Keiyoro, 2016 sampling formula (s= (z/e)2). Quantitative data was computed from structured questionnaires administered to 61 staff members working in the selected biosocial projects and 61 beneficiaries from the biosocial projects besides qualitative in- depth interviews with 61 key informants from State and non-state actors through purposive sampling technique. The statistical tools of analysis that were used were arithmetic mean and the standard deviation for descriptive data whereas Pearson’s Product Moment Correlation (r) in addition to Stepwise Regression (R2) were used as inferential statistics tools of analysis, hypothesis was tested by use of F-tests. To avoid invalidation of statistical analysis, tests of statistical assumptions were carried out before data analysis. From the data analysis the null hypothesis that stated the relationship between legal frameworks and performance of biosocial projects in informal settlements in Nairobi County is not moderated by political environment was accepted with F = 15.207, p =0.000<0.05, r = 0.382, Adjusted R2 = 0.136 in step one against step two where F = 6.263, p =0.000<0.05, r = 0.390, Adjusted R2 = 0.128 and concluded that Adjusted R2 decreased from 0.136 to 0.128 and F statistics reduced from 15.207 to 6.263 the effect of relationship of legal frameworks on performance of biosocial projects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".