A New Forensics: Developing Standard Remote Sensing Methodologies to Detect and Document Mass Atrocities
Bibliographic record
Abstract
Aim: The aim of this article is to highlight potential methods applicable to a standard forensic approach for the analysis of high-resolution satellite imagery that may contain evidence of alleged mass atrocities. Methods: The primary method employed is the retrospective analysis of a case study involving the use of high-resolution satellite imagery analysis to document alleged mass atrocities. The case study utilized herein is the Satellite Sentinel Project’s reporting on the May 2011 sacking of Abyei Town by Government of Sudan-aligned armed actors. In the brief case study, categories of objects, patterns of activities, and types of alleged mass atrocity events are applied the Abyei Town incident. Results: Categories of activity patterns, visible phenomena, and relevant objects leaned from the Abyei Town case study may provide a scalable example of how accepted forensic standards for remote sensing analysis of alleged mass atrocities may be further developed. Conclusions: The methods and frameworks applied in this research to the Abyei Town case study should be tested and refined through further case studies. The sources of these case studies may be both past reports by civil society, governments, and international judicial bodies and new analyses of previously unanalyzed high-resolution satellite imagery of alleged mass atrocities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".