Norms, Institutions and UN Reform: The Responsibility to Protect
Bibliographic record
Abstract
The Outcome Document produced at the 2005 UN World Summit reveals both the promise and the potential incoherence of reform efforts in the UN. Although the member states were not able to agree on how to treat such fundamental questions as nuclear proliferation and representation on the Security Council, they did agree in principle on key structural changes to the UN system, such as the creation of a Peacebuilding Commission and the metamorphosis of the Human Rights Commission into a Human Rights Council. While the Peacebuilding Commission was established in December 2005 through parallel resolutions of the General Assembly and Security Council, the design of the Human Rights Council was left for future negotiations which have already proven to be exceedingly difficult. \n \nAlthough the member states could not agree on a definition of terrorism or on a set of criteria for the authorization of military force by the Security Council, they did agree on one normative innovation that has the potential for transformative impact in international law and politics: the responsibility to protect. In this essay, we assess the reform potential of the responsibility to protect. We place that assessment in a context of failure to agree on institutional reform initiatives. We ask why states were able to articulate the responsibility to protect, but we also ask whether or not that articulation is likely to have any meaning when institutional reforms seem stuck.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.090 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| 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".