Stature is a biological trait directly determined by the interaction of genetic and environmental components. As such, it is often evaluated as an indicator for the reconstruction of skeletal biological profiles, past health, and social dynamics of human populations. Based on the analysis of 549 skeletons from the CAL (Collezione Antropologica LABANOF), a study of the diachronic trend of male and female adult stature in Milan (Italy) is being proposed here, covering a time span of about 2000 years, ranging from the Roman era to present-days. The skeletons, from necropolises dedicated to the less wealthy classes of Milanese society, were assigned to one of following five historical periods: Roman Era (first-fifth centuries AD), Early Middle Ages (sixth-tenth centuries AD), Late Middle Ages (eleventh-fifteenth centuries AD), Modern Era (sixteenth-eighteenth centuries AD) and Contemporary Era (nineteenth-twentieth centuries AD), and their stature was estimated according to the regression formulae of Trotter (1970). The collected data were then subjected to statistical analyses with ANOVA using R software. Although stature values showed an ample standard deviation in all periods, statistical analyses showed that stature did not significantly vary across historical periods in Milan for both sexes. This is one of the rare studies showing no diachronic changes in the trend of stature in Europe.
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http://dx.doi.org/10.1038/s41598-023-28406-5 | DOI Listing |
Am J Hum Biol
January 2025
Department of Anthropology, Lomonosov State University, Moscow, Russia.
Objectives: A meta-analysis of diachronic changes in average height across Europe from the Mesolithic to the present, based on a broad range of literature sources.
Materials And Methods: The analysis of chronological height variability was based on skeletal remains (from the Mesolithic to the 19th century), from which height was reconstructed, and on data from living individuals measured from the late 19th to the early 21st century. In total, data from 73 skeletal series and 342 groups of modern populations, primarily from Eastern Europe, were analyzed.
PLoS One
July 2024
PREMEDOC Research Group, Departament de Prehistòria, Arqueologia i Història Antiga, Universitat de València, València, Spain.
In this paper, we concentrate on the neolithisation process in Mediterranean Iberia through a diachronic view (from 8600-6800 cal. BP), focusing on social interaction as a factor in articulating new cultural ties. To do this, we apply techniques centred on similarities in material culture by applying Social Network Analysis (SNA).
View Article and Find Full Text PDFJ Ethnobiol Ethnomed
July 2024
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Venice, Italy.
Sci Rep
July 2024
Grupo de Sistemas Complejos, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro 2-4, 28040, Madrid, Spain.
In this article, we present the findings of a comprehensive longitudinal social network analysis conducted on Twitter across four consecutive election campaigns in Spain, spanning from 2015 to 2019. Our focus is on the discernible trend of increasing partisan and ideological homogeneity within interpersonal exchanges on this social media platform, alongside high levels of networking efficiency measured through average retweeting. This diachronic study allows us to observe how dynamics of party competition might contribute to perpetuating and strengthening network ideological and partisan homophily, creating 'epistemic bubbles' in Twitter, yet showing a greater resistance to transforming them into 'partisan echo-chambers.
View Article and Find Full Text PDFPLoS One
April 2024
ERA Chair for Cultural Data Analytics, Tallinn University, Tallinn, Estonia.
Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicability of large language models for automated stance detection in a challenging scenario, involving a morphologically complex, lower-resource language, and a socio-culturally complex topic, immigration.
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