Drivers of Livelihoods Diversification in Rungwe District
Bibliographic record
Abstract
This paper examined the factors influencing livelihoods diversification in Rungwe district. Specifically, the study assessed the livelihoods activities in the study area and determined the drivers of livelihoods diversification. The study was carried out in six villages of Rungwe District, Mbeya Region in which about 253 households were interviewed for the study. Data was collected through documentary review, household interviews, focused group discussions (FGDs), key informant interviews (KIIs), transect walks and field observation. Quantitative data were analysed using SPSS version 20 and Excel spreadsheet. While chi-square test was conducted to determine the associations between influencing factors and livelihoods activities, content analysis was used to analyse qualitative data. The study results showed that there were various livelihoods activities in the study villages such as cash crop production, livestock keeping, trade and wage labour. Livelihoods diversification was influenced by factors like markets, climate, population, land shortages, institutions, policies, and livelihoods assets. However, agriculture has remained the main occupation of households in Rungwe District. Moreover, market appeared to be a strong factor in influencing livelihoods diversification in the area than any other factors. On that basis, this paper recommends that livelihoods activities which were environmentally friendly should be encouraged. Additionally, markets and transport services should be improved to provide equal opportunities for diversification among rural populations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".