Water and politics in Africa: The need for regional cooperation
Bibliographic record
Abstract
This paper examines the need for regional cooperation in water resources management in Africa. The earth is a water planet made up of 70% water, but the crucial fact is that freshwater constitutes only 3%, 99% of which is locked in polar icecaps, glaciers and far too deep underground, leaving humanity with only 0.3% with which to quench its ever increasing thirsts. At the country level, extreme variability exists in the availability of total renewable water resources. It ranges from 10 m3 in Kuwait to 100,000 m3 in Canada. Also, there is high variability in time within the year for water availability. The 2 major extremes, namely: water poor and water rich countries include Brazil, Russia, etc., which are generally water rich, while Israel, Jordan, etc., are usually the water poor. in the water poor nations, water scarcity has reached ‘stress’ level; although the absolute level of water is said to remain the same, the globe is indisputably facing a growing level of water scarcity. Africa has about 60 international rivers, with a comparatively few international agreements on the use of water courses. Some of these are: Congo basin – 5 agreements; Incomati – 6; Limpopo – 2; Niger – 10; Nile – 19; comparatively, Europe has only 71 international river basins but with about 200 agreements. The world environment has been greatly plundered and many negative consequences are now emerging. This has created more water resource problems across the world with Africa as one of the worst hit. Water is gaining strategic importance across the world. Many conflicts are emerging due to water resources. Many water conflicts hotspots now dot the world, with several of such in Africa. It is saddening that Africa with many international rivers, has no serious water use agreements. The only way out of the present crises is for African countries to emerge with strong efforts at cooperation for sustainable water resource management. Key words: Regional co-operation, resources management, fresh water.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".