The UN at war: examining the consequences of peace-enforcement mandates for the UN peacekeeping operations in the CAR, the DRC and Mali
Bibliographic record
Abstract
The UN peacekeeping operations in the Central African Republic (CAR), Democratic Republic of Congo (DRC) and Mali were in 2013 given peace enforcement mandates, ordering them to use all necessary measures to ‘neutralise’ and ‘disarm’ identified groups in the eastern DRC and to ‘stabilise’ CAR and northern Mali. It is not new that UN missions have mandates authorising the use of force, but these have normally not specified enemies and have been of short duration. This article investigates these missions to better understand the short- and long-term consequences, in terms of the willingness of traditional as well as Western troop contributors to provide troops, and of the perception of the missions by host states, neighbouring states, rebel groups, and humanitarian and human rights actors. The paper explores normative, security and legitimacy implications of the expanded will of the UN to use force in peacekeeping operations. It argues that the urge to equip UN peacekeeping operations with enforcement mandates that target particular groups has significant long-term implications for the UN and its role as an impartial arbitrator in post-conflict countries.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".