Deconstructing Biological Sex by Fuzzifying Osteological Sex: Implications for Theoretically Informed Practice

Area of Science:

• Forensic Anthropology
• Bioarchaeology
• Biological Anthropology

Background:

• Current methods for sex estimation in forensic and bioarchaeological contexts are limited to binary categories (female/male).
• These binary methods reinforce normative conceptions of sex and gender, excluding non-binary variations.
• This binary approach fails to capture the full spectrum of human biological variation.

Purpose of the Study:

• To explore natural patterns of sex-related variation in skeletal data using a novel analytical approach.
• To challenge the limitations of binary sex estimation in forensic and bioarchaeological studies.
• To develop a more inclusive framework for understanding biological sex variation.

Main Methods:

• Utilized cranial and postcranial metric data from the Forensic Data Bank for assigned females and males.
• Applied Unsupervised Possibilistic Fuzzy C-Means (UPFCM), a fuzzy clustering technique.
• Explored patterns in cranial, postcranial, and combined datasets to identify natural groupings.

Main Results:

• Generated 38,691 models, identifying optimal clustering solutions (e.g., six-cluster for combined/postcranial data).
• Many individuals were classified as "outliers" with low typicality, indicating variation beyond defined clusters.
• Clustering demonstrated bimodal variation with overlap between assigned females and males, reflecting significant intra- and interindividual variation in sex expression.

Conclusions:

• Fuzzy logic principles allow individuals to partially belong to multiple groups, aligning with complex sex-related variation.
• This research demonstrates fuzzy principles can shift sex frameworks from binary to bimodal and continuous models.
• The findings offer a means to address issues of biological normalcy and rigid constructions of sex and gender.