Bibliographic record
Abstract
For over a decade, shifting geopolitics, a changed global security environment, and countless failures of diplomacy have raised important questions on how to collectively grapple with a widely-perceived “crisis of multilateralism,” and reignited debate on the fitness of the UN Security Council to deliver on its mandate under these circumstances. Among other issues, ongoing polarization when not outright “gridlock” among the Permanent Members (P5) has fueled the Council’s apparent inability to respond to threats to civilian life and to countless mass atrocities around the world. Even worse, as this article argues, some P5 today possess both the willingness and unprecedented means to themselves commit atrocity crimes virtually unchallenged. Scholarship, however, has yet to systematically examine how this might be affecting prevention and response efforts, particularly though not exclusively at the UN. Against this background, this article makes a first attempt at conceptualizing what I call Great Power Perpetrators, and their challenge to the furthering of human-protection and prevention objectives. Anchoring my analysis in current geopolitics, I draw from Barnett and Duvall’s “taxonomy of power” to highlight how Great Power Perpetrators possess unique “institutional,” “compulsory,” “structural,” and “productive” forms of power that may not only defy traditional approaches to atrocity prevention and response; but, when used in combination to perpetrate abuse, also pose a “system-level” challenge to international security and cooperation. To illustrate this, I empirically analyze how the Russian Federation (which I consider to be the best contemporary illustration of the notion) wields its power with respect to other states and institutions on atrocity prevention and response issues, while also extending my analysis to other P5 as relevant. In light of said analysis, I conclude that leveraging alternative mechanisms within the prevailing multilateral system might be the best—if not currently the only—viable approach to confronting, curbing, and countering great power abuse.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".