Forging Consensus for Atrocity Prevention: Assessing the Record of the OSCE
Bibliographic record
Abstract
This essay examines the record of the Organization for Security and Cooperation in Europe (OSCE) in fostering norms and collaborative practices for preventing mass atrocities in Eurasia. Comprising fifty-seven participating states “from Vancouver to Vladivostok,” the OSCE is the sole regional security organization spanning all of the members of NATO and the former Warsaw Pact. Its consensus-based approach to advancing “common and comprehensive security” has proved successful in preventing escalation or containing levels of violence in various conflicts in the Baltic states, Ukraine, Southeastern Europe, and the Caucasus. Since the late 1990s, however, rising geopolitical tensions between NATO and the Russian Federation have undermined the effectiveness of the OSCE’s conflict prevention initiatives. In order for the OSCE to play a more robust role in enhancing human security in Eurasia, it will need to find a path toward rebuilding the normative consensus between Russia and its Western participating states.
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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 | 0.120 | 0.325 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".