Bandingkan kaedah

	HISCO Occupational Coding	Historical Nominal Record Linkage
Bidang	Social History	Historical Demography
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2002	2016
Pengasas	Marco H. D. van Leeuwen, Ineke Maas, and Andrew Miles	Ivan Fellegi and Alan Sunter (probabilistic theory); James Feigenbaum, Ran Abramitzky, Leah Boustan (historical ML methods)
Jenis	measurement-classification	measurement-linkage
Sumber perintis	van Leeuwen, M. H. D., Maas, I., & Miles, A. (2002). HISCO: Historical International Standard Classification of Occupations. Leuven University Press. ISBN: 9789058671967	Abramitzky, R., Boustan, L., Eriksson, K., Feigenbaum, J., & Perez, S. (2021). Automated Linking of Historical Data. Journal of Economic Literature, 59(3), 865-918.
Alias	HISCO coding, Historical occupational classification, Standardized occupational titling, Occupational title harmonization	Record linkage, Census linking, Fellegi-Sunter matching, Historical individual linkage
Berkaitan	3	3
Ringkasan	HISCO, the Historical International Standard Classification of Occupations, is a coding scheme that translates the messy free-text occupational descriptions found in historical records into a single standardized, hierarchical classification. Built by van Leeuwen, Maas, and Miles on the framework of the modern ILO ISCO-68, it lets a 'cordwainer' in eighteenth-century England, a 'cordonnier' in France, and a 'Schuhmacher' in Germany all resolve to the same shoemaker code. Occupational titles are the single richest indicator of social and economic position in pre-modern records, appearing in censuses, marriage registers, tax lists, and directories. But their idiosyncratic spelling, multilingual diversity, and archaic vocabulary make raw comparison impossible. HISCO provides the controlled vocabulary and coding rules that turn these strings into analysable data, forming the indispensable first layer beneath class schemes, mobility studies, and occupational structure reconstruction.	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.