Unmasking difference: intersectionality and smallholder farmers’ vulnerability to climate extremes in Northern Ghana
Bibliographic record
Abstract
This paper examines intersectionality and climate vulnerability in Ghana’s Upper West Region. Located within the southern fringe of the West African Sahel, and inhabited largely by smallholder farmers, the Upper West faces recurring climate extremes. This paper asks, how do the intersections between different inequalities and power relations shape vulnerability to climate extremes? Evidence for the paper comes from intensive qualitative fieldwork. Focusing especially on lived experiences from four case studies, the paper demonstrates the textured ways in which masculinity ideals, health status, religion, gender, age, marital status, and poverty intersect to deepen farmers’ vulnerability to dry spells, flash floods, and after-storm recovery. Overall, the paper advances two interrelated arguments. Firstly, it argues that vulnerability analysis that focuses independently on gender, class, religion, and other characteristics, is insufficient because it risks homogenizing entire groups. Secondly, the paper argues that climate extremes do not always affect women more adversely than men. Indeed, dominant ideals of threatened masculinity can make men highly vulnerable during extreme climatic events. In the end, the paper concludes that if vulnerability analysis fails to unmask difference or move beyond binary gender categories, ensuing interventions may miss the real needs of countless individuals.
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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 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".