EMPOWERING RURAL WOMEN FOR SUSTAINABLE DEVELOPMENT IN KENYA, OPPORTUNITIES AND CHALLENGES: CASE OF MIGWANI SUB-COUNTY, KITUI COUNTY, KENYA
Bibliographic record
Abstract
The purpose of this paper is to investigate the link between the empowerment of rural women and sustainable development. Women account for about half of the world’s population. However, despite decades of development and a push for democracy and good governance globally, women still remain among vulnerable groups in the world living in abject poverty and lacking access and control over resources. For instance, even though women constitute the majority in food production, only 20% of them own arable land globally. Even with the high population, their participation in public leadership, decisions and policymaking largely remains extremely skewed towards men. For that reason, some international agencies have strongly addressed themselves to the plight of women, working towards more gender equity. Thus, the only assured pathway to development sustainability is to ensure consideration of both women's and men’s voices and interests as well as having parity in the allocation of resources and opportunities. This paper entails a descriptive study whose data was collected from 100 respondents using a questionnaire. Both women and men were sampled to avoid any biases. The study findings indicated that; women were the majority in the informal sector with 86% compared to men who accounted for only 14%. However, despite their heavy presence in the sector, their educational status was very low with only 78.2% having attained primary school education, whereas the men had higher educational achievement with 76.9% having attained a secondary school education. In spite of the traditional strategies of empowerment adopted by the government and the private sector, the study established that women had their preferred strategies to empower themselves. For example, they valued the availability of saving cooperatives (95.4%); having a safe working environment for their business (95.4%); getting family support in doing business (91.5%); equitable access to relevant and timely information (95.4%); having facilities like table banking (95.4%); having training on decision-making (91.5%) and having access to credit facilities (90.8%). Therefore, the involvement of women in policy and decision-making on issues of their empowerment is critical in order to adopt and implement their voices and interests. Without women’s involvement and engagement, the realization of sustainable development cannot be attained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".