Using tiny artifacts to answer big questions: Machine learning, microdebitage, and household spaces at Tamarindito Phyllis S. Johnson Department of Anthropology, University of Kentucky, Lexington, KY, USA Markus Eberl , Rebecca Estrada Aguila Department of Anthropology, Vanderbilt University, Nashville, TN, USA Charreau Bell, and Jesse Spencer-Smith Data Science Institute, Vanderbilt University, Nashville, TN, USA Abstract The spatial analysis of microdebitage (measuring less than 6.3 mm) can identify areas where stone tools were knapped at archaeological sites. These tiny artifacts tend to become embedded in the locations where they were first deposited and are less vul- nerable to post-depositional movement, making microdebitage an important artifact class for identifying primary areas of stone tool production. Traditional microdebitage analysis, however, can take multiple hours spread over several days to complete. Because of this, microdebitage analysis is typically completed in very small areas of sites due to the intensive time and labor commitment required. Recently, however, my colleagues and I have developed a novel, interdisciplinary method that combines dynamic image analysis and machine learning to analyze microdebitage taken from soil Corresponding Author: Phyllis S. Johnson, Department of Anthropology, 108 Lafferty Hall, University of Kentucky, Lexington, KY 40506, USA. Email: Phyllis.s.Johnson@uky.edu Original Research Article North American Archaeologist 1–20 © The Author(s) 2022 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/01976931221121177 journals.sagepub.com/home/naa