Rethinking United Nations peacekeeping responses to resource wars and armed conflicts in Africa: integrating African indigenous knowledge systems
Bibliographic record
Abstract
Purpose As of November 2021, six out of the 12 United Nations (UN) peacekeeping operations are in Sub-Saharan Africa, spread between the Democratic Republic of Congo (DRC), Western Sahara, Mali, Central African Republic, Abyei, South Sudan and Darfur. When considered alongside other recent conflicts in Liberia, Angola, Sierra Leone, Côte d’Ivoire and Mozambique, many of these conflicts are driven and sustained by resource looting of oil, minerals, timber, gas and fertile land and sand. Although other factors, particularly colonialism, the creation of poorly governed states, ethnic polarization, greed and extremism contribute to violence, the author argues that resource looting is central. Taking the DRC as the case study, the purpose of this paper is to examine why traditional UN peacekeeping, grounded in the international liberal order, has failed to efficiently deescalate wars and armed conflicts that are driven by resource looting and how alternative homegrown peace strategies can be more effective. Design/methodology/approach Deploying peacekeeping, peacebuilding and resource governance and theories, this paper examines the current UN peacekeeping efforts to increase our understanding of how alternative peacekeeping strategies found in African cultures, particularly indigenous epistemologies can be used to engender sustainable peace and security. The second argument is that sustainable peace and security cannot be solely exogenous, without integrating African cultural heritage, specifically African indigenous knowledge systems or epistemologies, a factor that is consistent with people’s right to self-determination and agency. Findings Peacekeeping that is exogenously enforced has failed to create sustainable peace and security in the DRC. Originality/value To the best of the author’s knowledge, this paper is original, based on the research conducted in the DRC. Following the academic writing norms, the data is backed up by literature.
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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 | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".