Violent farmer–herder conflicts in Ghana: constellation of actors, citizenship contestations, land access and politics
Bibliographic record
Abstract
This paper presents evidence from two cases in Ghana (Agogo and Gushiegu) to examine why farmer–herder conflicts escalate into violence. It argues that aside from resource competition, and crop damage, there are multi-faceted and dynamic processes and factors involved. The cases show that such violent conflicts can be explained by a constellation of actors, politics, land access and citizenship contestations. Many actors are important in this escalation. Local citizenship discourses and framing of conflicts are intricately linked to struggles over access to land and resources through which actors mobilize for violence. Both farmers and herders see cattle owners, chiefs, politicians, local groups and government officials as responsible for mobilizing and inciting violence and clandestinely using politics and citizenship in access to resources. At the same time, farmer–herder conflicts generate internal struggles between those who have interests in cattle (herders/businessmen and chiefs/elders) and those who have no such interests (young first-comer “cattleless” farmers).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".