KENYA-UGANDA TRANSBOUNDARY DISPUTE RESOLUTION MECHANISMS WITH REGARD TO PROMOTION OF BILATERAL ECONOMIC SECURITY MANAGEMENT
Bibliographic record
Abstract
Transboundary disputes over Lake Victoria between Kenya and Uganda concern resources in the marine zone. This transboundary conflict has had a negative impact on managing economic security since it has not been adequately addressed in resolutions. This study evaluates the strategies for resolving transboundary disputes between Kenya and Uganda in light of strengthening bilateral economic security management. The study was influenced by the structural-functionalist philosophy, social conflict, and John Burton’s theory of conflict resolution. The research design used in the study was descriptive. The samples were chosen using simple random sampling methods and purposive sampling techniques. Philosophically, the work provided a foundation for future research and helped us comprehend transboundary issues and how to manage them. Findings indicated that mediation and conciliation were the most used mechanism in resolving disputes in Lake Victoria. However, this approach has not reach zenith as far as managing dispute between Kenya and Uganda is concerned. The study recommends that, sensitization programs tailored towards effective dispute management be put into place and awareness programs disseminated along contested boundaries. In this regard, economic security management will be bolstered well.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".