Measuring Memberships in Collectives in Light of Developments in Cognitive Science and Natural- Language Processing

被引:1
作者
Hannan, Michael T. [1 ]
机构
[1] Stanford Univ, Stanford, CA 94305 USA
关键词
categorization; group membership; machine learning; CULTURE;
D O I
10.15195/v9.a19
中图分类号
C91 [社会学];
学科分类号
030301 ; 1204 ;
摘要
Which individuals and corporate actors belong in a collective, and who decides? Sociology has not had good analytical tools for addressing these questions. Recent work that adapts probabilistic representations of concepts and probabilistic categorization to sociological research opens opportunities for making progress on the measurement of memberships. It turns out that the probabilistic cognitive-based reformulation reveals unexpected connections to language models and natural-language processing. In particular, the leading probabilistic classifier BERT provides new and powerful ways to measure core concepts.
引用
收藏
页码:473 / 492
页数:20
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