Forensic uncertainty, fragile remains, and DNA as a panacea: an ethnographic observation of the challenges in twenty‐first‐century Disaster Victim Identification
Bibliographic record
Abstract
Abstract This is an account of ethnographic research examining the specialist scientific processes known as ‘Disaster Victim Identification’ (DVI) in three settings: Québec, the United States, and the United Kingdom. In cases of multiple deaths, a series of actions accompanied by a plethora of tools are often invoked, housed at a disaster scene, forensic laboratories, a family assistance centre, and a mortuary. In this article, I examine a process dedicated to connecting the biological remains of the deceased with a confirmed validation of personhood. I describe a situation where responders/scientists will attempt multiple testing and re‐testing of human remains, often pushing boundaries of available science. I argue that the search for certainty in identification lies at the heart of the activation of DVI processes, particularly when it is connected to DNA testing. Observing intimate forensic settings and the bricolage of the forensic anthropologist's labour has allowed me to track the production of the science of identity. I then reflect on the wider implications of these observations for affected communities and the responding scientists. Finally, I argue that there is complexity and ambivalence surrounding the increased use of technologies when applied to identification of victims.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".