The R2P and atrocity prevention: Contesting human rights as a threat to international peace and security
Bibliographic record
Abstract
Abstract The significant link between human rights violations and the eventual outbreak of atrocity crimes has been widely promoted across the UN system. However, the question of how the connection between the R2P norm and human rights plays out in the actual practices and debates of the UN Security Council has been relatively under explored. In response, the article builds on constructivist research into norm robustness in order to trace how the R2P's shift to an atrocity prevention focus has generated increased applicatory contestation over the push to expand the link between human rights and threats to international peace and security. Based on extensive analysis of UN Security Council meeting records and three case studies, the article highlights two competing ideological frames that currently divide the Security Council's approach to atrocity prevention. This division has emphasised a key disconnect between the work of the Security Council and other UN institutions such as the Human Rights Council, therefore severely limiting the potential for effective atrocity prevention responses. Thus, without a stronger connection to human rights in the process of threat identification, the R2P norm will remain considerably limited as a prevention tool. Consequently, the article also contributes to a new understanding of the critical role evolving institutional rules and practices play in state attempts to both constrain and reshape human protection norms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| 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".