The Fall and Rise of Identified Reference Collection: It Is Possible and Necessary to Transition from a Typological Conceptualization of Variation to Effective Utilization of Collections
Bibliographic record
Abstract
In some jurisdictions, race, ancestry or population affinity are part of the biological profile used in preliminary identification, for historical and political reasons. It is long overdue for forensic anthropologists to abandon this typological approach to human variation, regardless of the terms used. Using a sample (n = 105) selected from the Terry and Coimbra identified reference collections, a blind experimental approach is used to test several metric methods and versions of methods for group estimation (Fordisc 3.0 and 3.1, and AncesTrees), that rely on different statistical approaches (discriminant function analysis and random forest algorithms, respectively) derived from different reference samples (Howells’ data in AncesTrees and Fordisc 3.1, and different forensic subsamples in Fordisc 3.0 and 3.1). The accuracy for matching premortem documented group designation is consistently low (36 to 50%) across testing parameters and consistent with other independent tests. The results clearly show that a change in terminology, software updates, alternative statistics, expanded reference samples, and newer collections will not solve the underlying fundamental problems. It is possible and necessary to transition from a typological conceptualization of variation to the effective utilization of identified reference collections in Forensic Anthropology. In addition to the theoretical and methodological reasons, it is unethical for forensic anthropologists to continue to use on the deceased methods that do not work and that serve only to further exclude and marginalize the living.
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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 | 0.234 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".