Community-led nature-based solutions for enhancing climate change preparedness and resilience in semi-arid environments
Bibliographic record
Abstract
Smallholder farmers in sub-Saharan Africa (SSA) face multiple climatic stressors, poverty, and longstanding economic and environmental challenges. In Ghana, a Nature-based Solution (NbS) initiative called Community Resource Management Area (CREMA) has emerged as a community-led conservation effort with a linked binary objective of natural resources conservation within the bounds of CREMAs and local livelihood enhancement. However, empirical evidence remains limited and unclear regarding how CREMA improves livelihoods and builds a resilient future. Guided by Social-Ecological Systems (SES) theory, our study investigates the relationship between CREMA as an NbS and Climate Change Preparedness (CCP) and Climate Change Resilience (CCR) in the semi-arid Upper West Region of Ghana. We utilized ordered logistic regression to analyze 517 smallholder farmers' cross-sectional data. Our findings showed that the CREMA approach significantly (p<0.001) enhanced CCP and CCR. The findings highlight that the CREMA has the potential to be scaled up as an NbS initiative for climate adaptation in the semi-arid northwestern Ghana within the Global South.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".