Assessing CREMAs’ Capacity to Govern Landscape Resources in the Western Wildlife Corridor of Northern Ghana
Bibliographic record
Abstract
Ghana initiated community resource management areas (CREMAs) as a community-based natural management approach to give local communities the right and power to manage natural resources within their territorial boundaries. The expectation is that communities and their environment would prosper through more equitable landscape governance and sustainable use of natural resources. However, the challenges to achieving full functionality of CREMA and expected results, particularly in the Western Wildlife Corridor in northern Ghana, raise questions about the governance actors' capacity. Therefore, this study aims to assess the capacity of actors to take ownership of and lead the governance processes implied by the CREMA approach. Based on focus group discussions and individual interviews, we found that the capacities of the CREMA governance bodies are weak to implement the CREMA approach effectively. The lack of knowledge and technical skills to support multi-actor processes, the weak collaboration between actors, and the lack of sustainable financial inflows and livelihood support are key challenges to be addressed for better CREMA performance. Despite these constraints, local actors' enthusiasm and willingness to engage more actively in the governance of their landscape constitute an opportunity for an improved implementation of the CREMA approach. We suggest that initiatives to strengthen the technical and financial capacities of governance bodies and raise awareness among the local population are necessary to improve the functioning and performance of CREMAs. In addition, actions to improve the livelihoods of local communities will enhance the mobilization and engagement of social groups in the implementation of the CREMA concept.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".