Promoting a Bi-partisan Approach in Responding to the Contemporary Socio-economic Challenges in Kenya.
Bibliographic record
Abstract
The contemporary political landscape in Kenya has been marred by political antagonism and unhealthy competition among the political actors. The Kenyan citizens have bore the brunt of the negative outcomes of this approach to political engagement in seeking solutions to their socioeconomic challenges like poor roads, lack of access to health and educational facilities especially in marginalized areas, high crime rate, inflation, unemployment among others. There is need for a paradigm shift to ensure positive socio-economic outcomes are achieved. This paper therefore seeks to demonstrate how a bi-partisan approach in responding to the contemporary socio-economic challenges can help achieve socioeconomic development and milestones in Kenya. This study is grounded on political theory while the Search, Appraisal, Synthesis and Analysis (SALSA) framework was used to review different articles from revered journals related to bipartisan policy, non-confrontational political approaches and pragmatic political ideals. The reviewed literatures revealed that achieving bipartisanship in a competitive political environment is a tedious process but ultimately if achieved produces positive socio-economic outcomes like fast tracking of bills and policies in parliament meant to ensure provision of services to the people, value addition to suggested ideas and minimal obstruction in the implementation of projects and services to the people. The study recommends a wide stakeholder engagement and intensive training of Non state actors like the Civil Society groups, State actors like elected representatives at the, Constituency, County and National assembly on the value of bipartisanship and how to engage positively on issues of common good. These bipartisan ideals that will enhance their service delivery and ensure socio-economic development at the constituency. County and National levels.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".