From conflict to collaboration: Atewa Forest governance
Bibliographic record
Abstract
Abstract The problem of forest degradation and loss has become the concern of many countries. To address this challenge, some collaborate in sustainable forest management. The most successful outcomes, however, are observed where local participation is an essential part of conservation efforts. In Ghana, forests have experienced various degrees of exploitation over the years, resulting in their ecological decline. Despite its designation as a protected area for biodiversity and ecosystem services, the Atewa Range Forest Reserve in Ghana has been significantly impacted by deforestation, illegal mining, and other destructive activities. The purpose of this paper is to examine ecologically based management approaches that could be adopted to generate beneficial outcomes for all forest stakeholders and actors in Ghana. The study sampled forest stakeholders in Kwabeng, the administrative capital of the Atewa West District, to understand forest governance challenges and outline strategies for overcoming them. The study revealed that a bottom‐up all‐inclusive approach to managing forest resources is necessary. This paper, therefore, proposes an integrated forest governance that prioritizes the UN Sustainable Development Goal 15—Life on Land‐related to forest preservation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".