Dehumanizing R2P: Preventing Mass Atrocities without Human Security?
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
In 2005, the United Nations General Assembly unanimously adopted the World Summit Outcome Document, including three key paragraphs articulating the international community’s responsibility to protect (R2P) civilians from mass atrocities, including war crimes, genocide, crimes against humanity, and ethnic cleansing. Some key controversies and shortcomings of the current R2P principle remain absent from the debate, namely the lack of a human-centered approach from which R2P can be more adequately understood and implemented. I will make three key arguments following from this gap. Firstly, the original articulation of R2P developed by the International Commission on Intervention and State Sovereignty (ICISS) in 2001 sought to locate itself largely within the human security discourse to overcome the bureaucratic and “national-interest” obstacles of P5 decision-making on international peace and security as it relates to mass atrocities. Secondly, the state-centric articulation of R2P adopted in the World Summit Outcome Document in 2005, endorsed by the United Nations Security Council in 2006, has reified the inherent problem associated with allowing the UN Security Council the exclusive right to make key global security decisions on egregious crimes perpetrated against civilians. Thirdly, without embedding R2P within the human security discourse, specifically understanding human beings as the referents of security, the principle offers very little significant normative or political progress on the protection of civilians, and will continue to fall short as a galvanizing call to action to prevent mass atrocities, and save civilian lives.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".