Income Generating Activities of Rural Kenyan Women
Bibliographic record
Abstract
The proportion of female-headed households is rising dramatically in sub-Saharan Africa, making women’s income generating activities an increasingly important area of study. As women transition into the role of head-of-household, their traditional activities are augmented with the responsibility of being the breadwinner, and their successes become inextricably linked to the wellbeing of the entire household. In order to create sustainable development programs and policies that support women in this new role, an understanding of women’s current income generating activities must be established. This article seeks to do that through answering two questions. First, how do rural Kenyan women earn a livelihood? And second, what influences a woman’s decision to spend time, sweat, and energy on certain income-generating activities? The findings suggest that there are several underlying factors influencing women’s livelihoods and livelihood-related choices. Some of these factors include prioritizing relationships over occupation, identifying positive factors about their current income generating activities (i.e. comfort, extra food, flexible schedule), and planning for dependents rather than themselves. Moreover, the level of education and number of income generating activities directly impact the total income. These findings allow us to better understand the motivations and influences over the choices of work, as well as initiate a conversation on micro franchise opportunities in developing nations.
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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 | 0.000 | 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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".