National Mechanisms for the Prevention of Atrocity Crimes
Bibliographic record
Abstract
The field of atrocity crimes prevention has witnessed a trend over the previous three to four years in which states around the world are employing a new approach to the development and implementation of preventive policies. This trend has partly manifested in the establishment of what are called National Mechanisms for Atrocity Crimes Prevention. The Auschwitz Institute for Peace and Reconciliation (AIPR) among others, through its work supporting governments and their institutions to develop or strengthen policies and practices for the prevention of genocide and other mass atrocities, has been working with members of these National Mechanisms and following their efforts. This article presents an overview of the authors’ research in this area and the work of a number of National Mechanisms existing in two global regions, Latin America and the Great Lakes Region of Africa. After reviewing what the national architectures for prevention are, the article presents a critical overview of the successes, challenges, and existing opportunities of the Mechanisms.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".