Mitigating the Challenges Related to the Implementation of the Convention on Biological Diversity in Ghana
Bibliographic record
Abstract
In our world today, the control over and the use of a country’s natural resources (and the biological diversity of which they are a part) usually present a lot of challenges for both policy makers and implementing agencies and institutions. These challenges range from weak institutional capacities and technocratic hurdles to opposition from local communities for whom policies may be meant for. However, if such challenges are effectively mitigated, large prospects usually associated with the sustainable use and management of these natural resources may be realised. In this article, based on intensive interview of experts and critical review of official reports and policy documents, we identify a number of challenges associated the implementation of the Convention on Biological Diversity (CBD) in Ghana and recommend ways of addressing these challenges. The study finds that there is usually a wide knowledge and information gap on issues related to biodiversity in Ghana. Moreover, there is inadequate funding which also leads to the inability to retain relevant experts. In addition, there is the complex nature of implementing multilateral environmental agreements in Ghana and the lack of adequate publicity on the essence of the CBD. Key among the recommendations we make are effectively engaging civil society organisations on issues of biodiversity conservation and sustainable development; the enhancement of Alternative Livelihood Projects (EnALPs); stringent enforcement of punitive and preventive measures and; the implementation of finance-generating biodiversity services.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".