Behavioural insights on climate information uptake in Tanzania, Burkina Faso and Malawi
Bibliographic record
Abstract
The use of weather and climate information, or data and insights relating to both short- and long-term weather patterns in a specific region, has been encouraged to better understand, address, and mitigate the impacts and challenges presented by climate change. However, despite ongoing efforts to improve the development and availability of climate information, it is not well understood whether this information is made obvious to relevant users, and the extent to which climate information is utilised for improved decision-making. Further, addressing the knowledge gap of why the uptake and use of climate information is low or not done despite being made available to users would be a valuable new contribution and a space for behavioural science, as it would question the notion that the provision of knowledge automatically leads to action. This research shares insights from the Behavioural Adaptation for Water Security and Inclusion (BASIN) project, which is funded by UK aid from the UK government and by the International Development Research Centre (IDRC), Canada, as part of the Climate Adaptation and Resilience (CLARE) research programme. This project underscores how more inclusive water security and equitable adaptation can be supported, and one of its core research questions focuses on examining community perceptions of climate information, whether and how actions are taken as a response of available information, and reasons why climate information was not used. This presentation summarises these findings based on responses to a series of focus group discussions and key informant interviews undertaken in Tanzania, Burkina Faso and Malawi. For instance, it underscores barrier and enabling factors affecting climate information use and highlights how climate information could be better packaged for increased use in crop planning and enhanced agricultural production as well as flood and drought management. Such insights thus offer a context for the need for behavioural interventions that could be helpful to assist improved decision-making and community practices on water security and climate adaptation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".