Toward a semiotic-Bayesian epistemology of skeletal trauma interpretation in forensic anthropology

Abstract

This article introduces a semiotic-Bayesian epistemological framework for the analysis and interpretation of skeletal trauma in forensic anthropology. The model integrates Peircean semiotics with probabilistic inference to provide a structured, reproducible approach to evaluating forensic evidence under conditions of uncertainty. Bone injuries are conceptualized as signs that maintain a causal relationship with the events that produced them; their interpretation begins with abductive reasoning, through which the forensic anthropologist formulates plausible hypotheses about the origin of the observed trauma. These hypotheses are subsequently organized through the abductive-deductive-inductive (ADI) cycle and formally evaluated using likelihood ratios (LR), derived from empirical frequency data reported in the published literature, to quantify the strength of the evidence. This proposal is illustrated through three forensic cases involving cranial blunt force trauma, cervicocephalic injuries associated with interpersonal violence, and close-range gunshot wounds. In each case, the framework enabled logical quantification of evidential weight, enhancing interpretive transparency, mitigating cognitive bias and facilitating communication within judicial contexts. The methodology proposed bridges qualitative, context-driven interpretation with quantitative rigor, thereby addressing longstanding epistemological challenges aligning anthropological practice with modern legal standards of admissibility, and highlighting the important role of the LR as a logically coherent and reproducible measure of strength.

HIGHLIGHTS • A semiotic-Bayesian epistemology for bone trauma analysis in forensic anthropology is introduced. • The model formally integrates trace interpretation (semiotics) with probabilistic reasoning. • Likelihood ratios quantitatively express the strength of evidence for competing hypotheses. • The framework improves logical transparency, reduces cognitive bias, and ensures reproducibility.