Early Warnings and Perceived Climate Change Preparedness among Smallholder Farmers in the Upper West Region of Ghana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The impacts of climate change are already pushing beyond the threshold for sustainable agriculture and rural livelihoods. In Sub-Saharan Africa, smallholder farmers are particularly vulnerable due to limited resources and adaptive capacity. Early warnings are critical in mitigating and reducing climate-related dangers and building resiliency. That notwithstanding, there needs to be higher coverage of early warnings in developing countries, and there is even less knowledge of their contribution to rural development. Using a cross-sectional survey involving smallholder farmer households (n = 517), this study investigates the relationship between early warnings and perceived climate preparedness in Ghana’s semi-arid Upper West Region. From ordered logistic regression presented as an odds ratio (OR), factors that influenced climate preparedness in the past 12 months before the study include exposure to early warnings (OR = 2.238; p < 0.001) and experiences of prior climate events such as drought (OR = 9.252; p < 0.001), floods (OR = 6.608; p < 0.001), and erratic rain (OR = 4.411; p < 0.001). The results emphasize the importance of early warning systems and various socioeconomic factors in improving the climate resilience of smallholder farmers in Ghana. In conclusion, the study puts forth policy suggestions worth considering.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it