From reaction to conflict prevention : opportunities for the UN system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it