Blue sensors : technology and cooperative monitoring in UN peacekeeping.
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
For over a half-century, the soldiers and civilians deployed to conflict areas in UN peacekeeping operations have monitored ceasefires and peace agreements of many types with varying degrees of effectiveness. Though there has been a significant evolution of peacekeeping, especially in the 1990s, with many new monitoring functions, the UN has yet to incorporate monitoring technologies into its operations in a systematic fashion. Rather, the level of technology depends largely on the contributing nations and the individual field commanders. In most missions, sensor technology has not been used at all. So the UN has not been able to fully benefit from the sensor technology revolution that has seen effectiveness greatly amplified and costs plummet. This paper argues that monitoring technologies need not replace the human factor, which is essential for confidence building in conflict areas, but they can make peacekeepers more effective, more knowledgeable and safer. Airborne, ground and underground sensors can allow peacekeepers to do better monitoring over larger areas, in rugged terrain, at night (when most infractions occur) and in adverse weather conditions. Technology also allows new ways to share gathered information with the parties to create confidence and, hence, better pre-conditions for peace. In the future sensors should become 'tools of the trade' to help the UN keep the peace in war-torn areas.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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