Shifting the Forensic Anthropological Paradigm to Incorporate the Transgender and Gender Diverse Community
Bibliographic record
Abstract
Forensic anthropology and, more broadly, the forensic sciences have only recently begun to acknowledge the importance of lived gender identity in the resolution of forensic cases, the epidemic of anti-transgender violence, and the need to seek practical solutions. The current literature suggests that forensic anthropologists are becoming aware of these issues and are working toward efforts to improve identification of transgender and gender diverse (TGD) persons. The scope of the problem, however, is not limited to methodology and instead can be traced to systemic anti-trans stigma ingrained within our cultural institutions. As such, we call on forensic anthropologists to counteract cisgenderism and transphobia and promote gender equity and inclusion in their practice. In this paper, we identify three areas in which forensic anthropologists may be positioned to intervene on cisgenderist practices and systems: in casework, research, and education. This paper aims to provide strategies for forensic anthropologists to improve resolution of TGD cases, produce more nuanced, gender-informed research, and promote gender equity and inclusion in the field.
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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 | 0.053 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.012 | 0.091 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".