The Use of Geospatial Imagery in Myanmar for Mass Atrocity Prevention
Bibliographic record
Abstract
This paper aims to explore to what extent the usage of geospatial imagery can serve as a tool for atrocity prevention in a context of armed conflict and post-conflict. While most attention has been paid to the use of geospatial imagery to document mass atrocities for advocacy and accountability purposes, less attention has attracted the potential of this technology as a preventive tool. In the case of Myanmar, a special interest is on how to advance in the use of geospatial imagery to guarantee the safe return of the Rohingya refugees and how to prevent acts of genocide after the coup d’état by the military junta in 2021. The paper argues that one of the greatest advantages of the geospatial technology is the ability to monitor human rights violations in a large scale without violating the territorial integrity of the state. Beyond advocacy efforts, geospatial imagery can have a greater value for conflict analysis and early warning by helping organizations performing on the ground to coordinate their responses. Despite having access to more sophisticated tools, like the use of geospatial imagery, to assist atrocity early warning, the political will to intervene remains crucial to adopt measures to prevent mass atrocities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".