Bibliographic record
Abstract
Abstract This article discusses the United Nations and its peacekeeping intelligence. The United Nations has become a player in the global intelligence game. Given the inability of the UN to live up to its peace and security ideals, the disinclination of nations to share intelligence with it, the ad hoc nature of its responses to global crises, and its reluctance to consider itself as an intelligence-gathering organization, the UN's increasing involvement in the global intelligence came as a surprise. However, the UN has privileged access to many of the world's conflict zones, through its peacekeeping operations (PKOs). Its uniformed and civilian personnel serve as the eyes and the ears of the world in many hotspots. They report the latest developments at the frontiers of the world order and in the midst of civil war. In previous years, the UN relied heavily on overt surveillance through overt human intelligence. It employed direct monitoring and direct observation. Although human intelligence has helped resolved conflicts, overt human intelligence is not sufficient. With the new mandate and the difficult and dangerous environment of many PKOs during the Cold War, the United Nations was forced to change and reform its approach to intelligence. The UN is now including imagery intelligence (IMINT) and signals intelligence (SIGINT) in their approach to intelligence and is currently developing intelligence structures within its missions. Topics discussed in this article include: case studies of peacekeeping operations of the UN in countries with conflict such as Korea, Namibia, and Congo; monitoring technologies of the institution; and intelligence cycle of UN.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".