Devolution, coordination, and community-based natural resource management in Ghana’s community resource management areas
Bibliographic record
Abstract
Two key trends in efforts to deliver linked social and ecological protected area outcomes are (1) the development of governance models that devolve decision-making authority and responsibility to the local level and (2) linking protected area ‘islands’ to larger governance landscapes. This paper centers on Ghana’s Community Resource Management Area (CREMA) protected area model, and assesses how CREMAs are evaluated at the local level, which actors are perceived to be important in CREMA management, and how linkages to non-local governance structures may influence CREMA outcomes. Using a mixed method approach, results show that CREMAs are generally seen as a mechanism by which local people can more transparently and freely participate in decision-making processes related to resource management. Respondents also felt that Chiefs and associated customary tenure institutions should play a central role in CREMA governance. On the other hand, links to non-local state actors were described as ineffective because of inadequate fiscal decentralization, weak/absent lower level governance structures and inattention to conservation and development as a distinct dual project. Respondents also noted that while CREMA governance structures provide a way to build linkages to non-local actors, there are missed opportunities to embed CREMA considerations in other non-local decision-making processes.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".