The Trinity of Violence in Northern Nigeria: Understanding the Interconnectedness between Frustration, Desperation and Anger for Sustainable Peace
Bibliographic record
Abstract
Violence is triggered by disagreements or contentious issues between two or more individuals, parties, regions or nations. The consequences of violence are often undesirable, leading to disease, malnutrition, starvation, moral decadence (deterioration), poor economic performance of governments, boundary disputes, tribal divisions, the wanton destruction of lives, properties and so on. This paper uses the principles of negative emotion to understand the concept of violence as it occurs in Northern Nigeria. The paper further derives theoretical explanations from the principle that individuals have the power to let peace prevail through a focused consciousness and common structures of intelligibility. The process was based on 10 principles of understanding violence, which were derived from 14 negative emotion indicators or factors of violence in the study setting. Subject matter experts (SME), including security agencies, private security experts, victims of violence and religious leaders, were consulted to determine the interconnectedness between these emotions in trends and patterns of violence. Social network analysis was used as a tool to map the dynamics of emotions, which identified three negative emotions: desperation, frustration and anger. These were ranked in order of their occurrence in conflicts and subsequent violence. The paper also suggests means to reduce violence and conflict by understanding this ‘trinity’ of violence in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".