Revivifying the Responsibility to Protect: Strengthening the Normative Consensus for Atrocity Prevention
Bibliographic record
Abstract
This paper examines weaknesses in the Responsibility to Protect (R2P) principle, drawing on the scholarly literature on norm diffusion and norm cooptation. Despite having been unanimously adopted by the UN General Assembly in 2005, the R2P doctrine almost immediately attracted criticism that it reinforced a neocolonial stance by nations of the Global North toward those of the Global South. This critique grew in force during the chaotic aftermath of the 2011 intervention in Libya, which had been authorized as an R2P mission. The Russian invasion of Ukraine, the Israel-Hamas conflict, and the intensifying competition between the United States and China have further complicated efforts to build international consensus around atrocity prevention missions. In addition to assessing the causes of the fragmentation of the R2P norm, the essay seeks to identify productive possibilities for strengthening the normative consensus among UN member states on behalf of civilian protection, based on insights from practitioners and the author’s experiences working on U.S. atrocity prevention initiatives.
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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 | 0.081 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.073 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".