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1...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python3.9.2和Gephi0.10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。展开更多
This research uses random networks as benchmarks for inferential tests of network structures. Specifically, we develop formulas for expected values and confidence intervals for four frequently employed social network ...This research uses random networks as benchmarks for inferential tests of network structures. Specifically, we develop formulas for expected values and confidence intervals for four frequently employed social network centrality indices. The first study begins with analyses of stylized networks, which are then perturbed with increasing levels of random noise. When the indices achieve their values for fully random networks, the indices reveal systematic relationships that generalize across network forms. The second study then delves into the relationships between numbers of actors in a network and the density of a network for each of the centrality indices. In doing so, expected values are easily calculated, which in turn enable chi-square tests of network structure. Furthermore, confidence intervals are developed to facilitate a network analyst’s understanding as to which patterns in the data are merely random, versus which are structurally significantly distinct.展开更多
文摘This research uses random networks as benchmarks for inferential tests of network structures. Specifically, we develop formulas for expected values and confidence intervals for four frequently employed social network centrality indices. The first study begins with analyses of stylized networks, which are then perturbed with increasing levels of random noise. When the indices achieve their values for fully random networks, the indices reveal systematic relationships that generalize across network forms. The second study then delves into the relationships between numbers of actors in a network and the density of a network for each of the centrality indices. In doing so, expected values are easily calculated, which in turn enable chi-square tests of network structure. Furthermore, confidence intervals are developed to facilitate a network analyst’s understanding as to which patterns in the data are merely random, versus which are structurally significantly distinct.
