A New Forensics: Developing Standard Remote Sensing Methodologies to Detect and Document Mass Atrocities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it