Making Maps to Make Peace: Geospatial Technology as a Tool for UN Peacekeeping
Bibliographic record
Abstract
This article analyses how United Nations peacekeeping operations are harnessing geospatial technology, including high-resolution satellite imagery and geographic information systems (GIS), in the furtherance of peace and security. We argue that it is strengthening the ability of peacekeepers to accomplish their mandated tasks, including the demarcation of international boundaries, support for the negotiation of peace agreements, stabilization, the protection of civilians, human rights monitoring, electoral assistance, support for the extension of state authority and the provision of humanitarian assistance. However, it remains to be seen how and to what extent UN peacekeeping can continue to grow and expand its geospatial capabilities. We identify several challenges of an operational and political nature that tend to impede its utilization. A key question in this regard is whether politics will prevent peacekeepers from exploiting recent advances in geospatial technology. We conclude and synthesize our argument by developing a simplified framework for determining when and under what conditions peacekeepers can effectively harness geospatial technology.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.004 |
| 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".