UN Peacekeeping at 75: Achievements, Challenges, and Prospects
Bibliographic record
Abstract
This year marks the 75th anniversary of what the UN itself understands to be its first peacekeeping operation. It is therefore an appropriate time to reflect on the track record of UN peacekeeping in its efforts to try to maintain and realize peace and security. Moreover, this milestone invites us to ponder what lies ahead in the realm of peacekeeping. For this reason, this forum article brings together both academics and UN officials to assess the achievements and challenges of UN peacekeeping over the past 75 years. Through a dialogue among peacekeeping scholars and practitioners, we hope to identify current trends and developments in UN peacekeeping, as well as explore priorities for the future to improve the effectiveness of peacekeeping operations in terms of achieving their mandate objectives, such as maintaining peace, protecting civilians, promoting human rights, and facilitating reconciliation. This forum article is structured into six thematic sections, each shedding light on various aspects of UN peacekeeping: (1) foundational principles of UN peacekeeping - namely, consent, impartiality, and the (non-)use of force; (2) protection of civilians; (3) the primacy of politics; (4) early warning; (5) cooperation with regional organizations; and (6) the changing geopolitical landscape in which UN peacekeeping operates.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".