Gender Inequality, Climate Change, and Armed Conflict: Exploring the Triple Challenges for Female Farmers in Northwestern Cameroon
Bibliographic record
Abstract
Armed conflict amidst climate challenges significantly impacts on men and women in sub-Saharan Africa. In dominantly agrarian communities, climate vulnerability aggravates food insecurity, especially for women whose rights to factors of production are defined by patriarchy and rigid norms. In Cameroon, small-scale agriculture is in the hands of ill-equipped farmers, mainly women confronting multiple crises including, land rights, capital, climate change, and recently, armed conflict. This paper examines how gender relations, climate change, and armed conflict intersects to impact women small farmers in a country with inadequate agricultural infrastructures. Interviews were conducted with farmers in Santa, a locality ridden by climate change and armed conflict. Findings suggest that climate variability has strained farmers’ outputs and armed conflict creates insecurity which affects farmers’ access to their farms, factors of production, and markets. Both endanger local productivity, livelihood, and food security. The situation of women farmers is exacerbated because of gender inequality and entrenched gender roles. Robust climate and conflict resolution measures are indispensable for the survival of these communities.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".