The End of the Liberal World Order and the Future of UN Peace Operations: Lessons Learned
Bibliographic record
Abstract
Abstract Global conflicts are becoming increasingly transnational and often involve non‐state actors. These trends mirror the diffusion of power that has resulted from globalization and the erosion of liberal hegemony since the late 1990s and early 2000s. Though projections vary among scholars, the future international system is likely to involve a diversification of actors exerting influence in all policy spheres, including conflict‐response and peace operations. The United Nations (UN) and other liberal actors, historically dominant in peace operations, must adapt to remain relevant in a future where their governance of operations, and the underlying liberal democratic goals on which they are based, can no longer be assumed. In light of this waning liberal international order, this paper examines the core lessons learned from the past 70 years of UN peace operations to infer what future UN peacekeeping might look like and what adaptations will be necessary for this new environment. In so doing, we prepare recommendations for a truly localized and contextualized approach to peace operations that is expansive, representative and non‐directive, ultimately necessitating the UN and other liberal actors to adopt higher risk tolerance and relinquish exclusive control over conflict‐response and peace.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".