JOURNAL ARTICLE
Juvenile Remains from the Winchester Anatomized Site, Massachusetts.
Published In: Forensic Anthropology (University of Florida), 2025, v. 8, n. 4. P. 231 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Keane, Megan A. Hill; Borreson, Bailey; Pokines, James T. 3 of 3
Abstract
A large burial assemblage of fragmented human and nonhuman remains and associated artifacts was disturbed during house construction and excavated in Winchester, Massachusetts, USA, in 2020 and was determined to be a deposition site for anatomized remains dating to as early as the 1850s. Due to their overall development, size, and provenience, n = 724 total elements/fragments were classified as juvenile, with a Number of Identified Specimens = 671. The juvenile elements accounted for 4.8% of the total human sample (n = 15,193). Including all unfused bones, the ages estimated for the juvenile/unfused individuals span from 22 prenatal weeks up to a maximum of 19 years. The total Minimum Number of Individuals for human remains from this site is 46: 35 adults (79.5%) and 11 juveniles (seven of fetal age [15.9%], three younger children [2.3%], and one adolescent [2.3%]). The overall proportion of juvenile remains (23.9%) is consistent with similar sites with anatomized remains and indicates that the remains of young individuals were routinely utilized in anatomical training during the late nineteenth century in the United States. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Forensic Anthropology (University of Florida). 2025/10, Vol. 8, Issue 4, p231
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:2573-5020
- DOI:10.5744/fa.2024.0016
- Accession Number:188542308
- Copyright Statement:Copyright of Forensic Anthropology (University of Florida) is the property of University of Florida, Board of Trustees and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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