From reaction to conflict prevention : opportunities for the UN system
Bibliographic record
Abstract
Introduction: Making Conflict Prevention a Priority - F.O. Hampson, K. Wermester, and D. Malone. * THE DYNAMICS OF WAR. * Diagnosing Conflict: What Do We Know? - A-M Gardner. * Containing Internal War in the 21st Century - T.R. Gurr. * Measuring the Societal Impact of War - M.G. Marshall. * Horizontal Inequalities as a Source of Conflict - F. Stewart. * CONFLICT PREVENTION: THE STATE OF THE ART. * Preventive Diplomacy at the UN and Beyond - F.O. Hampson. * From Lessons to Action - M.S. Lund. * Planning Preventive Action J.G. Cockell. * Reassessing Cases: Direct vs. Structural Prevention - P. Wallensteen. * Deconstructing Prevention: A Systems Approach to Mitigating Violent Conflict - T.P. Dress and G. Rosenblum-Kumar. * Tackling the Root Causes of Conflict: More Bark than Bite - E.C. Luck. * COMPARATIVE ADVANTAGES: PRACTITIONER PERSPECTIVES BEYOND THE UN. * The Role of Research and Policy Analysis - M. O'Neil and N. Tschirgi. * Development and Conflict: New Approaches in the UK - M. Kapila and K. Wermester. * Addressing Conflict: Emerging Policy at the World Bank - P. Cleves, N. Colletta, and N. Sambanis. * Electoral Assistance and Democratization - B. Save-Soderbergh and I.N. Lennarisson. * CONCLUSION. * Preventive Action at the UN: From Promise to Practice? - C.L. Sriram and K.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.061 | 0.012 |
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".