法医学杂志

法医学杂志 ›› 2024, Vol. 40 ›› Issue (3): 245-253.DOI: 10.12116/j.issn.1004-5619.2023.430803

• 论著 • 上一篇    下一篇

1991—2022年法医人类学遗骸识别文献计量学分析

马继伟1,2(), 黄平3, 张吉2, 余海星2,4, 曹永杰2,5, 杨孝通2,6, 熊剪2,7, 张怀瀚2,6, 仓勇8, 史格非2(), 陈丽琴1()   

  1. 1.内蒙古医科大学法医学教研室，内蒙古 呼和浩特 010030
    2.司法鉴定科学研究院 上海市法医学重点实验室 司法部司法鉴定重点实验室 上海市司法鉴定专业技术服务平台，上海 200063
    3.复旦大学法庭科学研究院，上海 200032
    4.西安交通大学医学部法医学院，陕西 西安 710061
    5.南京医科大学法医学系，江苏 南京 211166
    6.山西医科大学法医学院，山西 太原 030001
    7.贵州医科大学法医学院，贵州 贵阳 550004
    8.无锡市中级人民法院，江苏 无锡 214002
  • 收稿日期:2023-08-14 发布日期:2024-08-20 出版日期:2024-06-25
  • 通讯作者: 史格非,陈丽琴
  • 作者简介:马继伟（1998—），男，硕士研究生，主要从事法医病理学研究；E-mail：1126680695@qq.com
  • 基金资助:
    中央级公益性科研院所资助项目(GY2021G-12);国家自然科学基金青年资助项目(81801873);司法部司法鉴定重点实验室资助项目;上海市法医学重点实验室资助项目(21DZ2270800);上海市司法鉴定专业技术服务平台资助项目

Bibliometric Analysis of Forensic Human Remains Identification Literature from 1991 to 2022

Ji-wei MA1,2(), Ping HUANG3, Ji ZHANG2, Hai-xing YU2,4, Yong-jie CAO2,5, Xiao-tong YANG2,6, Jian XIONG2,7, Huai-han ZHANG2,6, Yong CANG8, Ge-fei SHI2(), Li-qin CHEN1()   

  1. 1.Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot 010030, China
    2.Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China
    3.Institute of Forensic Science, Fudan University, Shanghai 200032, China
    4.School of Forensic Medicine, Xi’an Jiaotong University, Xi’an 710061, China
    5.Department of Forensic Medicine, Nanjing Medical University, Nanjing 211166, China
    6.School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China
    7.School of Forensic Medicine, Guizhou Medical University, Guiyang 550004, China
    8.Wuxi Intermediate People’s Court, Wuxi 214002, Jiangsu Province, China
  • Received:2023-08-14 Online:2024-08-20 Published:2024-06-25
  • Contact: Ge-fei SHI, Li-qin CHEN

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摘要：

目的 对法医人类学遗骸识别研究领域的文献进行计量学分析，描述当前的研究现状并预测未来的研究热点。 方法 基于Web of Science信息服务平台（以下简称“WoS”）中核心数据库（Web of Science Core Collection，WoSCC）检索和提取的数据，分析1991—2022年遗骸识别研究的发展趋势和主题变化。运用python 3.9.2和Gephi 0.10对法医人类学遗骸识别相关研究的发文趋势、国家（地区）、机构、作者和主题进行网络可视化分析。 结果 获得法医人类学遗骸识别相关英文文献873篇。发表文献数量最多的期刊是Forensic Science International（164篇），发文最多的国家（地区）是中国（90篇），Katholieke Univ Leuven（荷兰，21篇）是发表英文文献最多的机构。主题分析结果显示，人类遗骸研究的热点是遗骸的性别鉴定和年龄推断，并且常用的遗骸是牙齿。 结论 法医人类学遗骸识别研究领域的发文量具有明显的阶段性，然而，国际合作与国内合作的范围尚显局限。传统的遗骸识别主要依赖于骨盆、颅骨和牙齿等关键部位。未来的研究热点将聚焦于利用机器学习和深度学习技术，对多种骨骼遗骸进行更为精准和高效的鉴定。

关键词: 法医人类学, 文献计量学, 遗骸, Gephi, Web of Science, 网络可视化, 中介中心性, 特征向量中心性

Abstract:

Objective To describe the current state of research and future research hotspots through a metrological analysis of the literature in the field of forensic anthropological remains identification research. Methods The data retrieved and extracted from the Web of Science Core Collection （WoSCC）， the core database of the Web of Science information service platform （hereinafter referred to as “WoS”）， was used to analyze the trends and topic changes in research on forensic identification of human remains from 1991 to 2022. Network visualisation of publication trends, countries （regions）, institutions, authors and topics related to the identification of remains in forensic anthropology was analysed using python 3.9.2 and Gephi 0.10. Results A total of 873 papers written in English in the field of forensic anthropological remains identification research were obtained. The journal with the largest number of publications was Forensic Science International （164 articles）. The country （region） with the largest number of published papers was China （90 articles）. Katholieke Univ Leuven （Netherlands， 21 articles） was the institution with the largest number of publications. Topic analysis revealed that the focus of forensic anthropological remains identification research was sex estimation and age estimation, and the most commonly studied remains were teeth. Conclusion The volume of publications in the field of forensic anthropological remains identification research has a distinct phasing. However, the scope of both international and domestic collaborations remains limited. Traditionally, human remains identification has primarily relied on key areas such as the pelvis, skull, and teeth. Looking ahead, future research will likely focus on the more accurate and efficient identification of multiple skeletal remains through the use of machine learning and deep learning techniques.

Key words: forensic anthropological, bibliometrics, remains, Gephi, Web of Science, network visualization, betweenness centrality, eigenvector centrality

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