Understanding Alternatives to Tobacco Production in Kenya: A Qualitative Analysis at the Sub-National Level
Bibliographic record
Abstract
Tobacco is a key cash crop for many farmers in Kenya, although there is a variety of challenges associated with tobacco production. This study seeks to understand alternatives to tobacco production from the perspective of government officials, extension officers, and farmers at the sub-national level (Migori, Busia, and Meru) in Kenya. The study analyzes data from qualitative key-informant interviews with government officials and extension officers (n = 9) and focus group discussions (FGDs) with farmers (n = 5). Data were coded according to pre-identified categories derived from the research aim, namely, opportunities and challenges of tobacco farming and alternative crops, as well findings that illustrate the policy environment that shapes the agricultural context in these regions. We highlight important factors associated with the production of non-tobacco agricultural commodities, including the factors that shape the ability of these non-agricultural commodities to serve as viable alternatives to tobacco. The results highlight the effect that several factors, including access to capital, markets, and governmental assistance, have on farmer decisions. The results additionally display the structured policy approaches that are being promoted in governmental offices towards agricultural production, as well as the institutional shortcomings that inhibit their implementation at the sub-national level.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".