Beyond participation: power relations, information flows, and collaboration in water governance: a case of the Pra River Basin, Ghana
Bibliographic record
Abstract
Effective stakeholder participation in water resources management presents both opportunities and challenges within river basin contexts. This study investigates how governance structures influence power relations, information flows and collaboration among stakeholders in Ghana's Pra River Basin. Through 41 interviews with institutional and community-level actors, including government agencies, NGOs, and community-based organizations findings reveal a centralized governance model dominated by state institutions. Despite their high interest, community-based actors remain marginalized due to a lack of clearly defined roles in water governance and weak institutional integration. The high centrality scores of state institutions highlight their dominant position in the network, where information flows primarily among them, limiting direct engagement with weaker community-based actors. This structural imbalance reduces opportunities for meaningful participation in water governance, reinforcing the marginalization of nonstate actors in decision-making. Addressing these governance challenges requires a shift towards polycentric governance models that decentralize authority ensure multi-stakeholder participation at different levels of water governance and institutionalize inclusive decision-making frameworks. This study highlights the need for more practical governance reforms to bridge the institutional disconnect between state and non-state actors within the river basin ensuring a more resilient, inclusive, equitable, and participatory water governance in the Pra Rive Basin.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".