Meaningful climate change communication: an analysis of women dry season farmers in Kuliyaa community of Ghana
Bibliographic record
Abstract
Access to climate change information can play a critical role in helping rural women adapt to climate change. Our research investigates the communication channels used for climate education among female dry-season farmers in the Kuliyaa community of northern Ghana. Particularly, we address access to climate change information among smallholder female farmers. We adopted a community-based participatory approach to guide the study. To achieve our research objectives of investigating climate change communication channels among smallholder female farmers, specific methods of in-depth interviews and focus group discussions were used to gather knowledge from smallholder female farmers. The research findings provide a well-rounded exploration of the unique climate change communication challenges female farmers face and the innovative approaches they have adopted to share climate change information. Specifically, the findings show that women have adopted innovative oral communication channels to disseminate and transfer climate knowledge among themselves. We also found that there is limited access to radio in the Kuliyaa community making them resort to group meetings to share knowledge and ideas empowering them economically in other livelihoods. The study discovered the community utilises gender inclusivity in decision-making. We recommend that the government and other relevant organisations to develop and implement a strategic policy on climate information dissemination with a focus on supporting farmers in rural communities of Ghana to mitigate climate change effects. Technologies can also be developed to aid in disseminating relevant information to farmers in rural areas as the available oral communication is less effective in disseminating useful information on climate 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".