Harnessing diverse knowledge and belief systems to adapt to climate change in semi-arid rural Africa
Bibliographic record
Abstract
Farmers in semi-arid regions have historically coped using long established practices such as place-based climate forecasting using observations. However, this is becoming less reliable with climatic changes. Meteorological forecasting based on numerical prediction provides an alternative that is also now widely available to enable adaptation. However, this climate information has constraints including uncertainty and a broad spatial and temporal scale. The use of these two sources of forecast information is also affected by farmer perceptions of its advantages and disadvantages as well as beliefs and social norms. This study uses the case of Bobirwa subdistrict in Botswana to investigate the role of traditional norms and religious beliefs in the use of place-based and national meteorological forecast information to inform adaptation. Semi-structured interviews were conducted with 82 farmers from 8 different communities. We found that whilst some farmers use national meteorological information, others use place-based forecast information only and some combine the two. We also found that certain religious beliefs and traditional norms prevent the use of national meteorological forecast information by some farmers. An integrated climate information system that is credible and accessible to farmers from different belief systems will provide opportunity for farmers to use this climate information to adapt better to climate variability and change.
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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 | 0.002 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".