The Responsibility to Protect as International Crimes Prevention
Bibliographic record
Abstract
In 2005 the UN’s World Summit endorsed the idea that its members have a responsibility to prevent and halt genocide, crimes against humanity, ethnic cleansing and war crimes. Insufficient attention has been paid to clarifying how the definitions and evolving jurisprudence relating to these international crimes can provide clarity in identifying the unlawful acts that the Responsibility to Protect seeks to prevent and to halt. Specifically, an analysis of the elements of the crimes establishes the following parameters: attacks directed against any civilian population, committed in a widespread or systematic manner, in furtherance of a state or organizational policy, irrespective of the existence of discriminatory intent or an armed conflict. This conclusion makes reference to four ‘crimes’ redundant: crime against humanity alone provides an appropriate framework for conceptualizing and implementing the Responsibility to Protect. Although analysts focused on international crimes tend to prioritize accountability, such an approach need not be reactive. The essence of the Responsibility to Protect is best characterized as international crimes prevention.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".