Dealing with climate change in semi-arid Ghana: understanding intersectional perceptions and adaptation strategies of women farmers
Bibliographic record
Abstract
Climate change has diverse physical and socio-economic implications for communities in semi-arid areas. While several studies have sought to understand the underlying power relations that shape adaptive capacities of rural farmers, fewer studies have focused on unpacking the differences within the different social groups. In this paper, we present a case study based on women smallholder farmers from semi-arid Ghana. It explores their nuanced perceptions of climate variability and highlights how gender intersects with other identities, roles and responsibilities to influence adaptation strategies and barriers to adaptation in the semi-arid context. Farm-level data was collected from 103 women farmers using semi-structured interviews, focus group discussions and key informant interviews. Rainfall patterns were perceived by the women farmers to be increasingly erratic and perceptions of average temperatures were that they are increasing. Adoption of adaptation strategies were influenced by socio-demographic factors such as age, marital and residential status, which also influenced decision-making and power dynamics within the household. The paper highlighted the complex relationships that mediate women farmers’ access to resources and influence their vulnerability to climate variability and change. Highlighting the intra-gender differences that shaped the adaptation options and adaptive capacity is a prerequisite for proper adaptation policy planning and targeting.
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| 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".