The impact of community-led conservation models on women's nature-based livelihood outcomes in semi-arid Northern Ghana
Bibliographic record
Abstract
Abstract With increasing human-induced environmental degradation, women's nature-based livelihood activities are threatened. In semi-arid northern Ghana, shea processing (i.e., shea butter, a derivative of shea nut from the shea tree), a vital women-dominated economic activity, is at risk as naturally occurring shea trees continue to decline in numbers and productivity. The decline of the shea tree's number and productivity and the ensuing biodiversity loss have sparked conservation efforts by governments and local communities. This includes community-led conservation models, which have recently gained traction in the Global South. Ghana implemented the Community Resource Management Areas (CREMA)—a community-led conservation model to improve biodiversity and ecosystem services, including shea trees conservation in response to climate change. Research has not explored the impacts of community-led conservation efforts on women’s nature-based livelihoods in Ghana. Using a mixed-methods approach involving surveys (n = 517) and focus group discussions (n = 8), this study explored shea productivity outcomes under CREMAs. Findings show that women residing in CREMAs had significantly better shea harvesting outcomes than those outside CREMAs (α = −53.725; P < 0.01). These findings demonstrate the potential for targeted conservation initiatives that are community-led, such as the CREMAs, to improve the conservation of economically significant naturally occurring trees like Shea. With the increasing impacts of climate change and environmental degradation, such models would be instrumental in achieving sustainable development goals like SDG5-gender equality, SDG10-reduced inequalities, SDG13-Climate action, SDG14-life below water, and SDG14-life on land.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".