Crucial Technologies for the Protection of Civilians by UN Peace Operations
Bibliographic record
Abstract
Abstract To protect people under attack, what kinds of tools do peacekeepers need? The United Nations is gradually gaining valuable experience with sophisticated technologies for protection of civilians (POC). However, most remain underused and underevaluated, especially attack helicopters, night vision devices, and nonlethal weapons. This article presents case studies of these three crucial tools to examine their utility and to identify their shortcomings. Attack helicopters are demonstrated as a powerful through ironic symbol and an important means of robust peacekeeping in Central African Republic. Night vision devices proved essential for POC in protecting Haitians from gangs in 2007. Nonlethal weapons, like those developed on the spur of the moment in the Democratic Republic of Congo, helped the UN deal with civilian threats without recourse to lethal force. All these proven technologies have helped peace operations save lives and thus need detailed study to gain lessons. Some novel but untested technologies are also introduced, including laser signaling and digital simulation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".