Gender-differentiated capture of agro-based climate adaptation interventions: implications for agricultural systems and practices in Cameroon’s Western highlands
Bibliographic record
Abstract
Abstract In climate change adaptation, studies exist on extension interventions in sub-Saharan Africa, albeit the dearth of scientific evidence on the differential “capture 1 ” of agro-based adaptation packages. This paper contributes to provide evidence by (1) analyzing the typology of agro-based climate adaptation packages, and (2) exploring gender variations in the capture of agro-based climate adaptation packages. We use key informant interviews (N = 89) and focus group discussions (N = 14) to obtain data, analyzed using content analysis. Variations were observed in the capture of agro-based adaptation packages introduced by state and non-state actors. While men (adult male) mostly employed dominant information, women (adult female) drew from group formation. Agro-based adaptation capture led to major shifts in agricultural systems in the western highlands from monocropping to mixed cropping, mixed farming and agroforestry systems. The results show changes in agricultural systems from monocropping to mixed cropping. It was observed that women (adult female) and youths (both male and female) capture adaptation strategies encouraged by state agencies than the men (adult male) who adopt various adaptation strategies by both state agencies and non-governmental organizations. While these findings shed light on the dynamics of gender differentiated capture, it further calls for an in-depth exploration of other factors which shape agricultural system change.
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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 | 0.001 |
| 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".