In the Eye of the Beholder? The UN and the Use of Drones to Protect Civilians
Bibliographic record
Abstract
The debate on the UN’s possible use of drones for peacekeeping took a turn in 2013 when the Security Council granted the Department of Peacekeeping Operations (DPKO) permission to contract surveillance drones for MONUSCO, its peacekeeping mission in the Democratic Republic of Congo (DRC). This article examines what drone capability may entail for UN peacekeeping missions. We find that surveillance drones can help missions acquire better information and improve the situational awareness of its troops, as well as inform decision-making by leadership, police, and civilian components of the mission. We see a significant potential in the use of surveillance drones to improve efforts to protect civilians, increase UN troops’ situational awareness, and improve access to vulnerable populations in high-risk theaters. The use of drones can dramatically improve information-gathering capacities in proximity to populations at risk, thereby strengthening the ability of peacekeepers to monitor and respond to human rights abuses as well as violations of international humanitarian law (IHL). Drones may also enable peacekeepers to maintain stealth surveillance of potential spoilers, including arms smugglers and embargo breakers. They could additionally improve UN forces’ own targeting practices, further contributing to the protection of civilians (PoC). Furthermore, we emphasize how drone capability significantly increases peacekeepers’ precautionary obligations under IHL in targeting situations: the availability of drones triggers the obligation to use them to gather information in order to avoid civilian casualties or other violations of IHL or international human rights law. There may soon come a shift among human rights groups, from being skeptical of the use of drones by UN peacekeepers to demanding that peacekeeping operations be equipped with surveillance drones for humanitarian and human rights reasons – shifting the current debate, which has focused largely on the negative impact of the use of drones, to a more balanced debate that considers more objectively what drones are and what they can be used for. Finally, the debate about armed drones looms on the horizon for the UN as well – and we outline some of the key dilemmas that the inclusion of such a capability will entail.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".