Assessment of livelihood assets and strategies among tobacco and non tobacco growing households in south Nyanza region, Kenya
Bibliographic record
Abstract
This study assessed household assets and livelihood strategies among tobacco-growing households in comparison to non-tobacco-growing households in the south Nyanza region, Kenya. It was meant to provide basic information that could be used to advice on local enforcement of Article 17 of the WHO Framework Convention on Tobacco Control (FCTC) through crop and livelihood diversification as an alternative strategy to tobacco farming. A multi-stage and stratified random sampling procedure was used to select and survey 440 households (i.e. 210 tobacco and 230 non-tobacco) from the study area. The survey was carried out using a standard questionnaire with both structured and non-structured questions which was supplemented by four Focussed Group Discussions. The study established that an annual net income of a non-tobacco farmer is higher than that of a tobacco farmer with an average annual difference of $ 198 which is a significant margin in rural areas. Moreover, a tobacco farming household spends more income ($ 35) per year on healthcare services than a non-tobacco household, an indication that the latter group is prone to illnesses. In terms of social life, tobacco farming is labour intensive and evidently encourages polygamy though to a large extent, it is also a common cultural practice in the area. It was also noted that majority of the non-tobacco farming households have better housing quality and educational levels, and higher enterprise diversity than their counterparts. In conclusion, although households engage in tobacco farming to improve their living standards, tobacco farming is basically responsible for poor and un-sustainable livelihoods in the region. Hence, there is need to provide other alternative livelihood strategies to tobacco-growing households.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".