Organizational Strategies for Addressing Barriers to Women’s Participation in Agricultural Cooperatives
Bibliographic record
Abstract
Most of the time, women's active participation is structural and stems from household and community sociocultural norms. Both the law and culture acknowledge males as the nominal owners of household assets in the vast majority of cases. Women consequently do not have equal access to money and benefits. Due to this lack of access, women's confidence is further undermined, which makes it rare for them to hold important positions in market-based agricultural and mixed cooperatives. The study adopted mixed method research design and targeted 45 registered dairy and coffee cooperative with a membership of 114,267 members in Kiambu County. Simple random sampling was used to sample 398 female members who participated in the study. Data was collected using questionnaires and key informant interview guides. Quantitative and qualitative data were collected. Quantitative data was analyzed using descriptive statistics while qualitative data was analyzed using content analysis. The findings indicated that Level of education and age have a significant influence on women participation with both variables having a p value of 0.000. The findings also indicate that organizational policy strategies significantly affect women participation in agricultural cooperatives at p=0.000 and r=0.33. The study recommends that agricultural cooperatives should institute policies that favor women participation such as coopting some women members.
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.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".