Biological anthropology has experienced rapid methodological advances in ancient DNA, stable isotope analysis, medical imaging, and osteological reconstruction, enabling high-resolution inferences about past human population structure, mobility, diet, disease burden, and life history variation. Yet higher-resolution data do not automatically yield higher-resolution explanations, because similar biological signals may reflect distinct selection pressures and exposure environments under different social institutions, subsistence strategies, warfare patterns, mortuary practices, medical behaviors, and age and gender norms. Much of the contextual information needed to constrain such interpretations exists in textual sources such as ethnographies and excavation reports, but it is difficult to retrieve systematically by concept and transform into comparable analytical units that can be linked to quantitative bioanthropological data. This review examines methodological strategies for using eHRAF World Cultures and eHRAF Archaeology, the online databases of Human Relations Area Files (HRAF), not merely as background references but as reproducible text corpora for bioanthropological research. We describe the logic of paragraphlevel subject indexing and concept-based retrieval enabled by the Outline of Cultural Materials (OCM), and propose decision rules for selecting and integrating eHRAF World Cultures and eHRAF Archaeology using a triangulation approach to control for ecological fallacies and time-averaging, given their distinct units of analysis (cultures versus archaeological traditions). We further synthesize four analytical approaches, context retrieval, variable construction, middle-range theory building, and design scaffolding, and provide a stepwise workflow covering query logging, extraction unit definition, codebook development, intercoder reliability assessment, aggregation rules, unit alignment across cultural, temporal, and spatial scales, and strategies to address non-independence (Galton’s problem). We conclude by discussing key limitations, including representativeness, recording bias, temporal mismatch, and licensing constraints, and outline future directions such as leveraging OCM as labels for weakly supervised natural language processing (NLP) and artificial intelligence (AI) to enhance scalable and transparent biocultural inference.