Mapping Dissent: The Responsibility to Protect and Its State Critics
Bibliographic record
Abstract
Addressing dissent, also known as ‘rejectionism’, will broaden and deepen the global consensus on the Responsibility to Protect (R2P) principle. However, how should scholars understand the objections raised by state critics? To answer this question, I analyse R2P opposition as presented in official UN transcripts, voting records, and resolutions. The article reveals that six related themes of dissent exist with varying degrees of emphasis amongst opponents. Conventional depictions of R2P opposition, such as the absolute sovereignty or North vs. South explanations, are therefore inadequate representations of the diverse range of arguments employed by dissenters. Ultimately, I conclude that in order to build consensus at the expense of dissent, the principle should be further developed around four key notions: 1) non-coercive prevention and domestic capacity building, 2) enhanced prudential criteria for intervention, 3) global norm entrepreneurship from the Global South, and 4) veto restraint in R2P scenarios.
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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 | 0.044 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".