Enabling Conditions for Nature-based Solutions for Climate Adaptation in the Guinean Forests of West Africa: Evidence From Côte d’Ivoire, Ghana, and Guinea
Bibliographic record
Abstract
This article presents findings of the baseline survey for the project “Nature-based solutions for climate adaptation in the Guinean forests of West Africa” in the context of relevant legal, policy, institutional, and gender equity issues in six biodiverse and climate-change sensitive areas in three countries: Côte d’Ivoire, Ghana, and Guinea. Quantitative and qualitative data suggests that there are differences between the perceptions and/or experiences of male and female actors of many aspects of nature-based climate adaptation, such as perceptions of vulnerability to climate change, levels of participation of women in planning and implementing climate adaptation and reforestation activities, and knowledge of the concept of biodiversity. The data suggests that statutory and customary laws and local norms generally support women’s rights to actively participate in nature-based solutions such as agroforestry, although there are challenges in ensuring that women are fully involved in planning such activities, and benefit fully from them.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".