Data Sovereignty & Forensic Investigative Genetic Genealogy (FIGG): A Path Forward For Humanitarian & Mass Graves Investigations
Bibliographic record
Abstract
This paper addresses several issues concerning the ethical governance of forensic investigative genetic genealogy (FIGG) in humanitarian investigations that seek to identify decedents in mass graves, disaster victims, and to reconstruct past atrocities. FIGG is better suited to human remains investigations than existing forensic DNA methods, such as partial matching in CODIS databases, and for this reason its use has increased. However, survivor communities may not benefit from the use of FIGG to reconstruct past events and promote the goals of healing and reconciliation unless we first address several pressing issues with the ethical governance of FIGG in humanitarian investigations. These include a lack of trust on the part of survivor communities, concerns over privacy, autonomy, informed consent, and the future uses of genetic data that generate an unwillingness to provide DNA for forensic investigations. This paper looks at the movement of Indigenous data sovereignty, which posits that control over data should be put in the hands of those who are most affected by its use, and its potential to be used as a blueprint for the ethical governance of FIGG in all humanitarian investigations. This is illustrated through recent examples of data sovereignty being applied by FIGG investigators: the private, non-profit DNA Justice database, and the mass graves investigations at the Mother and Baby Home in Tuam, Ireland.
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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 | 0.186 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".