Сравнение методов

	Quantitative Prosopography	Historical Nominal Record Linkage
Область	Social History	Historical Demography
Семейство	Process / pipeline	Process / pipeline
Год появления	1971	2016
Автор метода	Lawrence Stone (programmatic statement); roots in Lewis Namier and Ronald Syme	Ivan Fellegi and Alan Sunter (probabilistic theory); James Feigenbaum, Ran Abramitzky, Leah Boustan (historical ML methods)
Тип	network-tabular	measurement-linkage
Основополагающий источник	Abramitzky, R., Boustan, L., Eriksson, K., Feigenbaum, J., & Perez, S. (2021). Automated Linking of Historical Data. Journal of Economic Literature, 59(3), 865-918.	Abramitzky, R., Boustan, L., Eriksson, K., Feigenbaum, J., & Perez, S. (2021). Automated Linking of Historical Data. Journal of Economic Literature, 59(3), 865-918.
Другие названия	Collective biography, Prosopographical network analysis, Large-scale biographical databases, Career-sequence prosopography	Record linkage, Census linking, Fellegi-Sunter matching, Historical individual linkage
Связанные	3	3
Сводка	Quantitative prosopography studies a historical group by investigating the common characteristics of its members through a collective analysis of their lives. Rather than writing one biography, the prosopographer defines a population, members of a parliament, a senate, a profession, a religious order, and poses a uniform set of questions to each life: social origins, education, marriage and kin, offices held, wealth, career path. The answers, encoded as structured data, are then analysed statistically and, increasingly, as networks, mapping the kinship, patronage, and office-holding ties that bound the group together. Programmatically articulated by Lawrence Stone and rooted in the work of Namier on Parliament and Syme on the Roman aristocracy, the method turns scattered biographical detail into comparable variables and relational graphs. In its modern, database-driven form it joins large biographical datasets to network analysis and statistics, illuminating how elites recruited, reproduced, and governed.	Historical nominal record linkage is the task of recognising when records in different sources, two censuses, a census and a draft register, a baptism and a marriage, refer to the same person, even though no shared identifier exists and names are misspelled, ages misreported, and places renamed. Linkage is the engine behind longitudinal historical micro-data: it builds the life-course panels that underpin studies of migration, mobility, mortality, and the long-run effects of early-life conditions. Three families of methods dominate. Deterministic linkage applies hand-crafted rules; the probabilistic Fellegi-Sunter framework weights field agreements and disagreements by their discriminating power; and supervised machine learning, trained on hand-linked examples, learns to classify candidate pairs. Modern historical practice, led by Abramitzky, Boustan, Feigenbaum, and collaborators, emphasises transparent, replicable algorithms and, crucially, explicit measurement of linkage error, since false matches and missed links can bias every downstream estimate.