Drones in the DRC: A Case Study for Future Deployment in UN Peacekeeping
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs), commonly known as drones, are often associated with the American War on Terror in the Middle East due to the extensive use of the technology for armed strikes and intelligence. It is well known that UAVs have been deployed in Pakistan, Iraq, Israel, and Afghanistan; however, few are aware that drones have played an integral role in the United Nations Organization Stabilization Mission in the Democratic Republic of the Congo (MONUSCO) since 2013. This paper seeks to empirically explore the implications of UAV use in the DRC by examining issues related to data use, mandated use of force, costs, as well as the blurring of offensive and humanitarian action. By using MONUSCO as a case study to examine its successes and pitfalls, this paper concludes that although the technology provides significant benefits, there are major obstacles for scaled-up use of drones in other missions. Ultimately, costs, missions that are less politically palatable, optics, and reputational risk are major challenges for the UN to consider in order to ensure fully executed mandates and successful missions where drones are involved in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".