A Non-Racial Approach to Assessing Group Membership of Victims in a Mass Grave Using Cranial Data
Bibliographic record
Abstract
In some jurisdictions, race, ancestry, or population affinity have been used for historical and po-litical, rather than biological, reasons in forensic anthropology when identifying individuals. The approach persists even though the genetic and skeletal data clearly demonstrate that human variation does not cluster into these groups. For over 60 years, these methods have consistently performed poorly when independently tested using large samples. By racializing the deceased, these methods have further marginalized the living. However, there is a need in the investigation of genocide and human rights violations to demonstrate that a specific group was targeted. Without relying on the outdated typological concepts of human variation, in this paper we present preliminary results for a method that can be used in a mass grave context to demonstrate that a specific group was targeted. Using samples from two identified reference collections, we created subsamples from one relatively homogeneous collection to model various mass grave scenarios and used the relatively heterogenous sample from the other collection as a reference for com-parison. In scenarios that varied by sample size and sex, it was possible to determine that a specific group was targeted if the sample size in a hypothetical mass grave was greater than 25 for a multi-sex sample, when sex is not known, and a minimum of 14 if sex could be estimated.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".