From conflict to collaboration through inclusive landscape governance: Evidence from a contested landscape in Ghana
Bibliographic record
Abstract
The Western Wildlife Corridor (WWC) in Ghana’s Northern Savannah ecological zone is a contested landscape where efforts to reverse widespread environmental degradation often conflict with local livelihood concerns and broader development objectives. Despite policy measures to devolve natural resource decision-making authority, poor environmental management, persistent socioeconomic challenges, and increasingly limited livelihood opportunities for people living within the corridor prevail. This study investigates environmental degradation in the WWC and natural resource governance using information on stakeholder perceptions from stakeholder workshops, focus group discussions, and key informant interviews. We also explore how natural resource management might be strengthened to better deliver social, economic, and environmental goals. We found that despite a history of contestation, stakeholders were able to agree upon specific issues of common concern and generate a collaborative vision for the WWC landscape. Transitioning toward such a vision requires significant investment in strengthening current governance structures and building natural resource management capacity within the corridor and beyond. Furthermore, persistent challenges of conflicting stakeholder objectives and issues related to coordination, corruption, and non-inclusion in decision-making about natural resources must be addressed to advance progress. Stakeholders were able to formulate specific recommendations and a participatory theory of change to inform the development of a sustainable landscape management plan and future evidence-based policy that could steer the WWC toward a more resilient and multifunctional system that equitably supports livelihoods, biodiversity, and wider economic development. The methods for inclusive engagement in environmental decision-making are extrapolatable to other contexts facing similar social-environmental challenges.
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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 | 0.008 | 0.020 |
| 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 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".