Bibliographic record
Abstract
BackgroundThe Framework of Analysis for Atrocity Crimes is a tool developed by the United Nations Office on Genocide Prevention and the Responsibility to Protect to guide the assessment of the risk of atrocity crimes 1 worldwide.This document builds upon the previous Framework of Analysis for the risk of genocide that was developed in 2009 by the then United Nations Office of the Special Adviser on the Prevention of Genocide, in order to fulfil its early warning mandate.That tool was based on the foundation laid by former United Nations Secretary-General Kofi Annan when he launched his plan of action to prevent genocide in April 2004.In the Secretary-General's words on that occasion:If we are serious about preventing or stopping genocide in the future, we must not be held back by legalistic arguments about whether a particular atrocity meets the definition of genocide or not.By the time we are certain, it may often be too late to act.We must recognize the signs of approaching or possible genocide, so that we can act in time to avert it.We badly need clear guidelines on how to identify such extreme cases and how to react to them.Such guidelines would ensure that we have no excuse to ignore a real danger of genocide when it does arise.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".