Analysis for Peace The Evolving Data Tools of UN and OSCE Field Operations
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
Abstract Both the United Nations and the osce are working to improve their peace operations technologically. While the emphasis is more often placed on new collection tools (e.g., satellite imagery, uav s, night-vision tools, etc.), the challenge remains to exploit the imagery and the copious other data that has been collected. By examining the software and evolving methods used by UN operations and the osce Special Monitoring Mission in Ukraine, we evaluate two often neglected steps of the information/intelligence cycle: analysis and dissemination. Lessons are drawn from both UN and osce experience in war-torn locations. Both organizations still need to establish strong and effective data-analysis and -sharing systems within their missions, and to find better ways to share information with the conflicting parties, and with humanitarian partners.
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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 | 0.000 |
| 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