Strengthening Climate Change Adaptive Capacity of Rural Women Crop Farmers through Reduced Social Exclusion in Nigeria
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
Climate change has continued to exert devastating effects on the Nigerian agricultural sector. Consequently, several efforts are made to adapt the agricultural sector to these effects of climate change, but the expected results are yet to be achieved. Much of the research on challenges to climate change adaptation were done without considering gender perspective. This review in effort to contribute to addressing this gap reviewed 1.) the climate change adaptation strategies used by rural women crop farmers in Nigeria, 2.) challenges to climate change adaptation among rural women crop farmers and 3.) social exclusion and influence on agricultural activities and climate change adaptation among rural women crop farmers in Nigeria. The study further suggests ways of eradicating social exclusion of rural women farmers with a view to strengthen their climate change adaptive capacity in the country. From the lessons highlighted, suggestions are made to make adaptation to climate change more gender-responsive, effective, and successful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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