Local participation in decentralized water governance: insights from north-central Namibia
Bibliographic record
Abstract
Abstract Although several semi-arid African countries are decentralizing water services and attempting to increase the participation of local actors in water resource management, how effectively this is working, and whether it is improving water access, is not yet well researched. Little attention has been paid to the capacities (in terms of knowledge and resources) that local actors need to successfully influence the operation and management of water services they are made responsible for. In a qualitative study, we asked regional and local actors in the Omusati Region of north-central Namibia for their perspectives on how water reforms, initiated in the late 1990s, have impacted on their participation in water governance. Our analysis reveals that decentralized governance of water resources can be ineffective if governments do not allocate sufficient resources to support and enable local actors to participate efficiently and effectively in the governance system. In the context of the Paris Agreement and the Sustainable Development Goals, achieving greater equity and efficiency in the water sector while reducing climate risk will require that local actors receive more support in return for fuller and more effective participation. We suggest that policy and practice around decentralized water governance pay more attention to building the capacities of local actors to absorb the responsibilities transferred to them.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".