The Gender–Climate–Security Nexus: A Case Study of Plateau State
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the gendered nexus between climate change, food insecurity, and conflict in Plateau State, Nigeria. This region in north-central Nigeria is marked by recurring farmer–herder clashes and climate-induced environmental degradation. Drawing on qualitative methods, including interviews, gender-disaggregated focus groups, and key informant discussions, the research explores how climate variability and violent conflict interact to exacerbate household food insecurity. The methodology allows the capture of nuanced perspectives and lived experiences, particularly emphasizing the differentiated impacts on women and men. The findings reveal that irregular rainfall patterns, declining agricultural yields, and escalating violence have disrupted traditional farming systems and undermined rural livelihoods. The study also shows that women, though they are responsible for household food management, face disproportionate burdens due to restricted mobility, limited access to resources, and a heightened exposure to gender-based violence. Grounded in Conflict Theory, Frustration–Aggression Theory, and Feminist Political Ecology, the analysis shows how intersecting vulnerabilities, such as gender, age, and socioeconomic status, shape experiences of food insecurity and adaptation strategies. Women often find creative and local ways to cope with challenges, including seed preservation, rationing, and informal trade. However, systemic barriers continue to hinder sustainable progress. This study emphasized the need for integrating gender-sensitive interventions into policy frameworks, such as land tenure reforms, targeted agricultural support for women, and improved security measures, to effectively mitigate food insecurity and promote sustainable livelihoods, especially in conflict-affected regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it