The effects of Egyptian imperial expansion into Nubia during the New Kingdom Period (1,550-1,069 BC) have been debated. Here, the impacts of the Egyptian Empire are investigated through an examination of osteological indicators of activity at the archaeological site of Tombos. Entheseal changes to fibrocartilaginous attachment sites and osteoarthritis are examined to infer what types of physical activities this colonial town was engaging in. Many of the skeletal remains at Tombos were commingled due to looting in antiquity; undisturbed burials are presented as a subsample of the population (n = 28) in which age, sex, and body size can be considered. The total sample (n = 85) is then analyzed to better understand overall levels of activity. A number of Nile River Valley bioarchaeological samples are used as points of comparison to the Tombos population. Results indicate that the inhabitants of Tombos had relatively low entheseal remodeling scores; this is highlighted when Tombos is juxtaposed with comparative samples, particularly in men. Furthermore, osteoarthritis, as assessed by eburnation, was also markedly infrequent at Tombos. Collectively, these results indicate a relatively low level of activity and support the hypothesis that Tombos may have served as an administrative center.

Located 10 km south of the Third Cataract of the Nile River, the ancient city of Kerma was once capital to the second largest state in Africa. The Eastern Cemetery at Kerma (∼4 km east of city center) encompasses 80+ hectares and was used over a period of 1,500 years (3,200-1,500 BC). Excavated in the early 20th century by George Reisner, the cemetery contained an estimated 20,000-40,000 individuals. Reisner classified these burials into multiple categories, including chiefs and human sacrifices, based on burial position and grave goods. This study investigates the skeletal embodiment of social inequality by examining variation in entheseal severity between the Kerma burial classifications. Seventeen entheses were examined using the Hawkey and Merbs (1995) scoring method (n = 205 individuals); age, sex, and body size variables were considered by employing Mann-Whitney U tests and partial Spearman's correlations. This analysis suggests that significant differences in entheseal changes existed between select burial types. Specifically, "corridor sacrifices" had significantly higher rates of entheseal changes while "chiefs" and "subsidiary burials" had similar entheseal changes; furthermore, within these burial categories, males had higher entheseal scores despite body size controls. The elevated entheseal changes in the sacrificial burials may be due to an intensive agro-pastoral lifestyle or other demanding forms of manual labor. In conclusion, the disparity of entheseal markers between burial subgroups at Kerma might reflect a degree of social inequality within this state level society. This bioarchaeological research informs our understanding of socially-defined categories of persons as well as everyday life in Ancient Kerma.

Sex estimation is an important part of creating a biological profile for skeletal remains in forensics. The commonly used methods for developing sex estimation equations are discriminant function analysis (DFA) and logistic regression (LogR). LogR equations provide a probability of the predicted sex, while DFA relies on cutoff points to segregate males and females, resulting in a rigid dichotomization of the sexes. This is problematic because sexual dimorphism exists along a continuum and there can be considerable overlap in trait expression between the sexes. In this study, we used humeral measurements to compare the performance of DFA and LogR and found them to be very similar under multiple conditions. The overall cross‐validated (leave‐one‐out) accuracy of DFA (75.76–95.14%) was slightly higher than LogR (75.76–93.82%) for simple and multiple variable equations, and also performed better under varying sample sizes (94.03% vs. 93.78%). Three of five DFA equations outperformed LogR under the B index, while all five LogR equations outperformed the DFA equations under the Q index. Both methods saw an improvement in overall accuracy (DFA: 86.74–95.79%; LogR: 86.74–95.76%) when individuals with a classification probability lower than 0.80 were excluded. Additionally, we propose a method for calculating additional cutoff points (PMarks) based on posterior probability values. In conclusion, we recommend using LogR over DFA due to the increased flexibility, robusticity, and benefits for future users of the statistical models; however, if DFA is preferred, use of the proposed PMarks facilitates future analysis while avoiding unnecessary dichotomization.

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