Ancestry Variation in the Accuracy of Rogers's Method of Sex Estimation
Bibliographic record
Abstract
Rogers’s (1999) method of human skeletal sex estimation evaluates morphological variation in four traits of the distal humerus. Although this method has the potential for widespread application in forensic and biological anthropological contexts, previous tests have been unable to replicate Rogers’s initial accuracy rate of 92%. Additionally, the role of ancestry in the accuracy of the method has not been sufficiently explored. This study expands on previous blind tests of Rogers’s (1999) original method, though it differs methodologically from prior studies (Ammer et al. 2019; Falys et al. 2005; Harrison 2017; Horbaly et al. 2019; Rogers 2009; Tallman & Blanton 2019; Vance et al. 2011; Wanek 2002; Watkinson 2012) by explicitly controlling for ancestry (85 American Black and 114 American White individuals, as defined in the Hamann-Todd Osteological Collection), by seriating humeri according to trait expression, and by using logistic regression in addition to chi-square and Fisher’s exact tests for analyzing the results. The findings determined that the method was 67% accurate overall and that correct classifications were 2.03 more likely for American Whites than American Blacks, posing an important consideration for practitioners of this method.
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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 | 0.073 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".