From Crisis to Reform: Peacekeeping Strategies for the Protection of Civilians in the Democratic Republic of the Congo
Bibliographic record
Abstract
The latest cycle of violence in eastern Democratic Republic of the Congo (DRC) and the brief occupation of Goma by the “M23” rebels call for a re-examination of how UN peacekeepers have approached the physical protection of civilians in the DRC over the past 13 years. This article examines how lessons from early protection crises led the UN missions in the DRC to develop a series of innovative tools for a better peacekeeping response based on improved civil-military coordination and enhanced communication with the local population. It analyzes how the need to mitigate the negative impact of joint UN-Congolese military operations led to a progressive shift from a largely UN-centric and troop-intensive approach to physical protection to a greater focus on the Congolese security forces. As the UN peacekeeping understanding of the protection of civilians – and its concomitant bureaucracy – continues to expand, peacekeeping strategies should refocus on strengthening national protection capacities through security sector reform. This article concludes that the 2012 crisis in DRC could serve as a trigger for such a shift, aimed at building legitimate institutions and encouraging the host government to shoulder its primary responsibility to protect its citizens. The new Intervention Brigade together with the Peace, Security and Cooperation Framework for the DRC and the region could provide the broader political strategy on which to anchor this reform process.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".