Buying Peace or Building Peace: Rethinking Non-Coercive Approach to the Management of Non-State Armed Groups involved in Mass Atrocity
Bibliographic record
Abstract
Non-state armed groups (NSAGs) are increasingly responsible for mas atrocities in contemporary armed conflicts. As agents with a monopoly on the legitimate use of force, the state has the responsibility to engage NSAGs for peace and security. How to sustainably engage them remains the subject of intense debate among policymakers and academics. While some advocate for the use of a coercive approach, others favor a non-coercive approach or a combination of both. Contemporary reality has shown states often opt for the adoption of a non-coercive approach to end mass atrocities and extreme violence because a coercive approach has proven to be counter-productive because it often escalates violence. The utility of this engagement approach to reduce the commission of mass atrocities and the extreme use of violence by armed groups remains a critical question that has not been interrogated academically, hence this study. Using case studies of the Presidential Amnesty Program (PAP) implemented in the Niger Delta region where armed conflict is rife and there are calls to grant amnesty to Boko Haram fighters in the North East Nigeria, this paper holds that it is not just enough to adopt a mono-dimensional non-coercive approach in engaging armed groups, but that any non-coercive approach must promote society-wide reconciliation and address core problems and the root causes of the grievances that developed into hostility and armed conflict. As suggested by the Niger Delta case, neither coercive nor non-coercive approaches are sufficient. Rather, a holistic reconciliation approach is needed. This is the only way the non-coercive approach can reduce mass violence and promote sustainable peace.
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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 | 0.002 | 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 | 0.000 |
| 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".