Population-specific variation in the accuracy of Rogers’ method of sex estimation
Bibliographic record
Abstract
Rogers’ (1) method of sex estimation is a visual technique that evaluates morphological variation in four traits of the distal posterior humerus. This method has the potential for widespread application in biological anthropology, but previous tests have been unable to replicate Rogers’ initial accuracy rate of 92%. Additionally, the role of populations in the accuracy of the method has not been sufficiently explored, as only one study (2) has controlled for it. Wanek (2) found differences in the accuracy of Rogers’ method correlated with different populations but concluded the method could be used on all human populations, regardless. This study tests Wanek’s (2) conclusion through a blind test of Rogers’ (1) original method, though it differs methodologically from previous studies (1–7) by seriating humeri according to trait expression, and by using logistic regression for analysis of results. In conducting a blind test on a sample of American black and white individuals from The Hamann-Todd Osteological Collection, I found that the method was 67% accurate overall, and that odds for a correct classification were 2.03 more likely for a white individual than for a black individual. Prior to applying this method in the future, bioarchaeologists and forensic anthropologists should consider these results within the context of their study.
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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 | 0.045 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".