Beyond Orthodox Strategies: Managing Conflicts and Sustaining Peace Through Communal Ethics, Traditional Values and Methodsin Africa
Bibliographic record
Abstract
The phenomenal rise in violent conflicts in Africa since the end of the Cold War has been an obstacle to peace and sustainable development. This paper observed that in spite of the proportionately high rate of resources expended on defence, internal strife and ethno-religious conflicts constitute features of most states in contemporary Africa. Much of the success of resolving and managing conflicts in Africa has been due in large part to the involvement of the international community. Through a variety of measures, from mediation,through sanctions to military interventions, many states in Africa have taken measures to bring an end to violence. Although this progress is laudable, the reality is that communal conflicts continue to ravage the continent. Despite the concerns surrounding international involvement in peace processes, this paper posits that the community of nations remains a necessary actor in managing conflicts and sustaining peace. Every African community has its own cultural strategies, institutions and values for monitoring, preventing, managing and resolving conflicts. The paper concludes that a homegrown peace approach within the context of communal ethics and traditional mechanisms should be incorporated in the methods of resolving conflicts and sustaining peace in contemporary Africa.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".