United Nations Peacekeeping Decisions: Three Hierarchies, Upward Mobility and Institutionalised Inequality among Member States
Bibliographic record
Abstract
This article explores the multiple and evolving hierarchies shaping UN decisions on peacekeeping operations. Three hierarchies—based on Security Council membership, financial assessments, and troop contributions—currently distribute influence over these decisions among UN member states. These hierarchies differ in their relationship to global stratification patterns, and in the states they empower. Their gradual “layering” has thus expanded the potential for upward mobility within the UN: states unable to increase their influence in one hierarchy can seek empowerment in another. Yet the UN peacekeeping case also highlights the limitations of hierarchy layering as an equalising mechanism in international organisations. New hierarchies supplement rather than replace older ones, and the degree to which they challenge existing rankings varies. Moreover, each new hierarchy inherently highlights, and creates institutional consequences for, a particular type of inequality among states. Consequently, hierarchy layering is best understood as recalibrating rather than eliminating institutionalised inequality in international organisations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".