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 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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".