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1.
Proc Natl Acad Sci U S A ; 121(4): e2305564121, 2024 Jan 23.
Article in English | MEDLINE | ID: mdl-38236732

ABSTRACT

Data from the distant past are fertile ground for testing social science theories of education and social mobility. In this study, we construct a dataset from 3,640 tomb epitaphs of males in China's Tang Dynasty (618-907 CE), which contain granular and extensive information about the ancestral origins, family background, and career histories of the deceased elites. Our statistical analysis of the complete profiles yields evidence of the transition away from an aristocratic society in three key trends: 1) family pedigree (i.e., aristocracy) mattered less for career achievement over time, 2) passing the Imperial Examination (Keju) became an increasingly important predictor of one's career achievement, and 3) father's position always mattered throughout the Tang, especially for men who did not pass the Keju. The twilight of medieval Chinese aristocracy, according to the data, began in as early as the mid-seventh century CE.


Subject(s)
Social Mobility , Social Sciences , Male , Humans , Pedigree , Educational Status , China
2.
Proc Natl Acad Sci U S A ; 117(15): 8398-8403, 2020 04 14.
Article in English | MEDLINE | ID: mdl-32229555

ABSTRACT

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.


Subject(s)
Social Sciences/standards , Adolescent , Child , Child, Preschool , Cohort Studies , Family , Female , Humans , Infant , Life , Machine Learning , Male , Predictive Value of Tests , Social Sciences/methods , Social Sciences/statistics & numerical data
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