Drivers and Consequences of Recurrent Conflicts between Farmers and Pastoralists in Kilosa and Mvomero Districts, Tanzania
Bibliographic record
Abstract
Recurrent conflicts between farmers and pastoralists have brought significant impacts on both groups. In response to this situation, the government and other actors have taken several measures to mitigate such conflicts with little success. This paper examined drivers and consequences of recurrent conflicts between farmers and pastoralists in Kilosa and Mvomero districts. Covering a sample size of 203 respondents, data was collected using interview, documentary review, and focus group discussions. Collected data was analyzed using both qualitative and quantitative analysis. The findings revealed that weak governance structures associated with unethical behavior, regulatory deficiencies, socio-economic and environmental factors are responsible for the recurrence of conflicts between farmers and pastoralists. Consequently, the recurrent conflicts have resulted into major socio-economic impact that includes loss of lives and properties to both farmers and pastoralists. Drawing from conflict and conflict resolution theories, which advocates use of coercive power and participatory approaches to restore peace, respectively; this paper conclude that no single strategy fits all conflicts given the complexity in which such conflicts occurs. In the light of the results this paper recommends that the effective way to address farmers-pastoralists conflicts; actors should use both lenses of coercive and participatory approaches and the choice of appropriate strategy will depends on the context since no single approach fits all types of conflicts...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".