Political Environment and Biosocial Projects Performance in Informal Settlements in Nairobi County, Kenya
Bibliographic record
Abstract
This research study strived to find out the influence of Political Environment on biosocial projects performance in informal settlements in the county of Nairobi. The extent to which political environment influence biosocial project performance. Biosocial projects are projects working with people with disabilities. Two theories, Theory of Constraint and diffusion were used in this field of study to support predictive and outcome variable respectively. Pragmatism paradigm and mixed research were adopted in this study projects. Quantitative data was collected through structured self-administered questionnaires while qualitative data was collected through interview guides. Collection of data was preceded by testing for validity of research instruments through reliability and content related method through test-retest criterion. In Nairobi County, a sample size of 183 individuals from 61 biosocial projects were selected from a target sample of 70 biosocial projects. Questionnaires were used to collect quantitative data from 61 beneficiaries of the biosocial projects and 61 staff members directly working for biosocial projects in the County of Nairobi. In- depth qualitative interviews with 61 state and non-state actors through purposive sampling technique were executed. Arithmetic mean and the standard deviation were the statistical tools of analysis that were used for descriptive data, whereas Stepwise Regression (R2) and Pearson’s Product Moment Correlation (r) were the statistical tools of analysis that were used for inferential statistics whereas F-tests were executed to test hypothesis. To avoid statistical analysis invalidation, statistical assumptions tests were executed before analysis of data. Null hypothesis after analysis of data analysis was rejected at r = 0.313, F = 8.988, p = 0.004<0.01. Conclusively, constitution of Kenya 2010 and the Persons with Disabilities Act, 2003 were some of the key legal legislation that were pointed out to be championing success of biosocial projects performance that champion for the rights of persons with disabilities.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".