Conflict Intensity in African Water Basins: Water Stress and the Effectiveness of Water Management Strategies
Bibliographic record
Abstract
Access to cross-border water sources in the African regions of the Nile River, Zambezi River, and Lake Turkana Basins becomes less certain as global population, human consumption, and climate change increase. Uncertainty during periods of high demand for water in agro-dependent economies creates circumstances of water stress, where social stability is low as stakeholders compete over scarce water sources. Longstanding traditions of political power, such as colonial rule and the status of regional superpowers, reinforce the unequal resource distribution. All three regions encounter water stress in the form of floods or droughts. They rely on dam projects that modify water distribution and basin agreements that reallocate political power to manage stress. The basins vary, however, in conflict intensity and effectiveness of water management strategies. The Nile River Basin exhibits low-intensity conflict and has institutionalized collaborative management strategies; the Zambezi River Basin demonstrates medium-intensity conflict with theoretically collaborative initiatives that fall short in practice; the Lake Turkana Basin exemplifies high-intensity conflict, lacking collaborative agreements. In order to address the discrepancy in outcomes, this study asks: what factors contribute to the intensity of conflict surrounding water stress? And, to what extent are water management practices effective in promoting cooperation and preventing conflict? The study concludes that the most intense conflicts occur in rural localities, where social instability is high and resource distribution is uneven. Collaborative agreements and international involvement in water management initiatives increase social stability and decrease conflict intensity by institutionalizing equitable distribution of water in a changing environment.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".