Vegetable Growers and Women Empowerment: Their Effect in Poverty Reduction in Region Korhogo (Côte d’Ivoire)
Bibliographic record
Abstract
In Côte d’Ivoire, women occupy an important place in the agricultural sector, which is the basis of the country’s economy. There is unanimous agreement on the essential role that these women must play through their empowerment for the country’s development. Therefore, we must find appropriate solutions to the problem of women’s empowerment in order to enable them to participate vigorously in the country’s development. This study aims to address the problems of women’s empowerment in agriculture by highlighting the potential opportunity that the practice of market gardening represents for women’s empowerment. Based on a qualitative approach using ENV (2019) data, the Women’s Empowerment in Agriculture Index (WEAI) shows that women producer of market garden produce are as self-reliant as their counterparts in other agricultural sub-sectors. In the same logic, the WEAI shows us that the practice of this crop has a positive effect on women’s empowerment. By using these four (4) methods of effect determination, namely the Nearest Neighbour Method, Radius Method, Stratification Method and Kernel Method, the following values were obtained: 0.007; 0.039; 0.017; 0.027, which are all positive. The objective of the study is to determine the impact of market gardening on the empowerment of the women who practice it.
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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 | 0.003 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".