Bibliographic record
Abstract
If the United Nations always succeeded or never succeeded in preventing atrocity crimes, then there would be no point in trying to improve its performance. Instead, its track record has been remarkably uneven. Its quiet successes at preventing mass violence have been more than matched by horrific and well-publicized failures to prevent (or protect). Though it is impossible to measure prevention with any degree of certainty, it appears that the world body has, on occasion, made a positive difference. So, it has potential. But, in too many situations, that potential has not been realized. This essay asks why the UN’s preventive efforts have been so inconsistent and how some of the shortcomings in its performance might be remedied. At the outset, this paper makes three assumptions. One, over the years, the United Nations has been no worse at preventing mass atrocities than have been regional and sub-regional organizations, governments, and/or civil society. Two, when prevention has worked, it has generally been because there has been productive collaboration among actors of these various types and levels, so credit or blame should be shared. Three, nevertheless, mediocrity on any actor’s part is not an acceptable standard when it comes to an issue of public policy with such existential implications for human life. This essay argues further 1) that the United Nations has a unique combination of assets that could be put to much better use in this area, 2) that shortcomings in its performance arise as much from conceptual misunderstandings and institutional dysfunction as from capacity deficits, 3) that these shortcomings have negative implications for whether and how effectively other critical actors respond to the atrocity prevention challenge, and 4) that steps could be taken to improve the situation significantly without a huge infusion of scarce resources. These points are addressed in the following four sections on potential, shortcomings, implications, and remedies, respectively.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".