Triggers of Farmer-Herder Conflicts in Ghana: A Non-Parametric Analysis of Stakeholders’ Perspectives
Bibliographic record
Abstract
In Ghana, farmer-herder conflicts have become widespread and increasingly assume a violent dimension. Competition over access to and use of land and water resources is at the center of the conflicts. However, competition does not automatically result in conflicts. The conflicts are driven by triggering activities of both farmers and herders. This study identifies triggers of farmer-herder conflicts in the Upper West Region of Ghana and tests the level of agreement among key stakeholder groups on the triggers of these conflicts. This is an important step in determining approaches to farmer-herder conflicts prevention and resolution. The data were collected via focus group discussions of five key stakeholder groups: chiefs-traditional rulers, Fulani herdsmen-cattle owners, crop farmers, civic society-media, and government agencies. Fourteen triggers of conflicts were identified by stakeholders, with destruction of crops by cattle ranking as the most important trigger. In testing agreement among stakeholder groups on triggers of conflicts, only crop farmers, chiefs-traditional rulers and government agencies significantly agree on the triggers of conflicts. There is also moderate level of concordance when the ranking of triggers of conflicts by all five stakeholder groups are simultaneously considered. The results show farmer-herder conflicts are complex and preventing and /or resolving these conflicts require integrated approaches.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".