International Law and the Responsibility to Protect: Clarifying or Expanding States' Responsibilities?
Bibliographic record
Abstract
The Responsibility to Protect (R2P) invokes one of the most powerful moral and legal terms in contemporary international politics – namely, responsibility. The nature of the relationship between R2P and international law and morality, however, remains contested, giving rise to questions lying at the core of R2P's normative foundations. What is the source of R2P? To whom is this responsibility attributable, and under what circumstances? Does R2P give rise to legal obligations? Such questions challenge International Relations (IR) theorists to look beyond their discipline for more insightful tools and methods of analysis. In this article, we apply a broadened theoretical framework to explain the ongoing controversy about R2P. In Part II, we borrow tools from moral philosophy to identify the source and the bearer of the responsibility to protect in today's international society. In Part III, we draw on international legal scholarship to analyse whether R2P has emerged as a 'new' norm of customary international law. We find that international endorsement of R2P has helped to clarify existing obligations in international law, but that intrinsic ambiguities in its articulation currently limit R2P's capacity to entrench new obligations for states to protect strangers. At the same time, our finding that R2P is an example of 'soft law' leads us to conclude that R2P can nonetheless exert significant influence on how states interpret their legal obligations and, in the coming decade, it may also help catalyse diplomatic efforts to reform the international architecture for preventing and responding to mass atrocities.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.080 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".