Bridging current research and best practices in identifying transgender skeletal remains in Canada
Bibliographic record
Abstract
Transgender and otherwise non-binary decedents face erasure from police statistics, forensic death investigation records and government databases. Current research shows concurrence on predictable skeletal changes that trans men and trans women experience due to hormone therapy and/or gender affirming surgery. These findings could help inform the identification of transgender individuals from skeletal remains, allowing for better tailored police investigations and closure to grieve for loved ones. In practice, Canadian forensic skeletal sex assessments and documentation are based on a strict male/female binary with minimal latitude for transgender (or otherwise non-binary) determinations. The lack of more inclusive, nationally streamlined sex and gender data conventions also increases the risk of transgender individuals being misgendered, and therefore misidentified or left unidentified in death, and left out of violent crime and missing-persons statistics, civilian death statistics and in archaeological contexts.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".