The Role of Meso-level Organizations in Climate Adaptation for Small-Scale Producers in Sub-Saharan Africa-insights from four African Countries
Bibliographic record
Abstract
Abstract Meso-level organizations (MLOs) are core climate change adaptation (CCA) actors responsible for interweaving micro-level rural community needs with macro-level policy and finance intentions. This study draws on qualitative data from four African countries: Ghana, Kenya, Malawi, and South Africa to examine the role of MLOs in CCA targeting small-scale producers. The findings reveal a diverse and complex landscape of organizational actors operating across varied geographic and social contexts, with significant differences in capacities and functions. These variations are reflected in four key dimensions: stability, flexibility, specialization, and autonomy, which are critical for enabling locally led adaptation initiatives. While many MLOs demonstrate relative stability, they are often highly dependent on a limited number of funding sources, constraining their autonomy. The analysis deepens understanding of how adaptation systems are configured across these contexts and suggests that effective local adaptation governance does not require all organizations to perform strongly across every dimension. Instead, it highlights the importance of complementary organizational roles and provides valuable entry points for strengthening existing climate adaptation organizational ecosystems in Sub-Saharan Africa.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".