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Categorical Engagement and the Contingent Nature of Typicality Effects

By on July 28, 2026 in Articles

Alex Tyulyupo, Balázs Kovács

Sociological Science July 28, 2026
10.15195/v13.a34

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Market categories can favor typical members over atypical ones, yet this “categorical imperative” operates inconsistently across contexts. We argue that typicality effects depend on how evaluators engage with categories during search. Using behavioral simulation in which participants search a large database of companies for competitors, we distinguish between searches that explicitly invoke industry classifications and those using alternative methods, and measure goal-category congruence through semantic alignment between evaluators’ objectives and the categories they employ. We find that typical companies become more likely to be selected in category-based searches; other searches produce no typicality effects. Among category-based searches, goal-category congruence moderates the effect: typicality strongly predicts selection when goals and categories misalign but becomes irrelevant when they align well. These findings identify a specific microprocess through which categorical effects become contingent, offering a process-level explanation for variation documented across audiences, producer characteristics, and evaluation contexts.

Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Alex Tyulyupo: Yale School of Management, Yale University.
E-mail: alex.tyulyupo@yale.edu.

Balázs Kovács: Yale School of Management, Yale University.
E-mail: balazs.kovacs@yale.edu.

Acknowledgments: We thank Ceclin Begbie and Robert Bartholomew for their management of the behavioral lab. We thank Maciej Workiewicz, Elisa Operti, and Stoyan Sgourev for their input. We gratefully acknowledge financial support from the Yale School of Management. We benefited from feedback received at the 2025 Nagymaros Conference and the Yale School of Management OBID seminar.


Supplemental Materials

Reproducibility Package: De-identified, model-ready data and code for reproducing all statistical tables and figures are available at (https://doi.org/10.17605/OSF.IO/6Z8RS). Raw Crunchbase records, company and venture descriptions, and participant-identifying information are not shared because of data-use and confidentiality restrictions.


  • Citation: Tyulyupo, Alex, and Kovács, Balázs. 2026. “Categorical Engagement and the Contingent Nature of Typicality Effects” Sociological Science 13:884-914.
  • Received: May 6, 2026
  • Accepted: June 23, 2026
  • Editors: Ari Adut, Clayton Childress
  • DOI: 10.15195/v13.a34


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Using Machine Learning to Uncover the Semantics of Concepts: How Well Do Typicality Measures Extracted from a BERT Text Classifier Match Human Judgments of Genre Typicality?

By on March 3, 2023 in Articles

Gaël Le Mens, Balázs Kovács, Michael T. Hannan, Guillem Pros

Sociological Science March 3, 2023
10.15195/v10.a3

Abstract

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Social scientists have long been interested in understanding the extent to which the typicalities of an object in concepts relate to its valuations by social actors. Answering this question has proven to be challenging because precise measurement requires a feature-based description of objects. Yet, such descriptions are frequently unavailable. In this article, we introduce a method to measure typicality based on text data. Our approach involves training a deep-learning text classifier based on the BERT language representation and defining the typicality of an object in a concept in terms of the categorization probability produced by the trained classifier. Model training allows for the construction of a feature space adapted to the categorization task and of a mapping between feature combination and typicality that gives more weight to feature dimensions that matter more for categorization. We validate the approach by comparing the BERT-based typicality measure of book descriptions in literary genres with average human typicality ratings. The obtained correlation is higher than 0.85. Comparisons with other typicality measures used in prior research show that our BERT-based measure better reflects human typicality judgments.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Gaël Le Mens: Department of Economics and Business, Universitat Pompeu Fabra (UPF), Barcelona School of Economics, and UPF Barcelona School of Management, Barcelona, Spain
E-mail: gael.le-mens@upf.edu

Balázs Kovács: School of Management, Yale University, New Haven, CT, USA
E-mail: balazs.kovacs@yale.edu

Michael T. Hannan: Graduate School of Business, Stanford University, Stanford, CA, USA
E-mail: hannan@stanford.edu

Guillem Pros: Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain
E-mail: guillem.pros@upf.edu

Acknowledgments: We are grateful to Jerker Denrell, Amir Goldberg, Greta Hsu, Thorbjørn Knudsen, Cecilia Nunes, and Phanish Puranam for discussion of ideas developed in this article and for the detailed feedback we received from them on the earlier versions. We thank conference participants at the 2021 and 2022 Nagymaros Conferences for valuable feedback and discussion. G. Le Mens and G. Pros received financial support from ERC Consolidator Grant #772268 from the European Commission. G. Le Mens also received financial support from grant PID2019-105249GBI00/ AEI/10.13039/501100011033 from the Spanish Ministerio de Ciencia, Innovacion y Universidades (MCIU) and the Agencia Estatal de Investigacion (AEI) and from the BBVA Foundation Grant G999088Q. B. Kovács was supported by Yale School of Management. M. Hannan was supported by the Stanford Graduate School of Business. Data, material, and analysis code for all analyses are available online at https://osf.io/ta273/. We encourage readers to download the shared folder and use the code to compute BERT typicality on their own data sets.

  • Citation: Le Mens, Gaël, Balázs Kovács, Michael T. Hannan, and Guillem Pros. 2023. “Using Machine Learning to Uncover the Semantics of Concepts: How Well Do Typicality Measures Extracted from a BERT Text Classifier Match Human Judgments of Genre Typicality?” Sociological Science 10: 82-117.
  • Received: September 28, 2022
  • Accepted: November 9, 2022
  • Editors: Ari Adut, Filiz Garip
  • DOI: 10.15195/v10.a3


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An Ecology of Social Categories

By on August 18, 2014 in Articles

Elizabeth G. Pontikes, Michael T. Hannan

Sociological Science, August 18, 2014
DOI 10.15195/v1.a20

Abstrac
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This article proposes that meaningful social classification emerges from an ecological dynamic that operates in two planes: feature space and label space. It takes a dynamic view of classification, allowing objects’ movements in both spaces to change the meaning of social categories. The first part of the theory argues that agents assign labels to objects based on perceptions of their similarities to existing members of a category. The second part of the theory shows that an object’s perceived similarity to members of other categories reduces its typicality in a focal category. This means that for categories with a high degree of overlap with other categories in label space (lenient categories), the link between feature-based similarities and labeling weakens. The findings suggest that social classification will likely evolve to contain both constraining and lenient categories. The theory implies that this process is self-reinforcing, so that constraining categories become more constraining, whereas lenient categories become more lenient.

Elizabeth G. Pontikes: University of Chicago. E-mail: elizabeth.pontikes@chicagobooth.edu.

Michael T. Hannan: Stanford University. Email: hannan@stanford.edu.

Supplemental Material

  • Citation: Pontkes, Elizabeth G. and Michael T. Hannan. 2014. “An Ecology of Social Categories.” Sociological Science 1: 311-343.
  • Received: April 15, 2014
  • Accepted: May 28, 2014
  • Editors: Olav Sorenson
  • DOI: 10.15195/v1.a20

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The Diffusion of the Legitimate and the Diffusion of Legitimacy

By on March 3, 2014 in Articles

Gabriel Rossman

Sociological Science, March 3, 2014
DOI 10.15195/v1.a5

Abstract
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This article models the implications of innovations being nested within categories. In effect, social actors assess the legitimacy of innovations vis-à-vis conformity to categories such that a sufficiently legitimate innovation may be adopted without direct reference to the behavior of peers. However, when innovations lack categorical legitimacy, actors default to proximately peer-oriented heuristics such as information cascades. Eventually, if enough similarly novel innovations achieve widespread popularity, their conventions will become accepted as a legitimate category. Thus density creates legitimacy, but this density can be at the level of the particular innovation or of the category within which it is embedded.

Gabriel Rossman: University of California, Los Angeles. E-mail: Rossman@soc.ucla.edu

Supplemental Materials

  • Citation: Rossman, Gabriel. 2014. “The Diffusion of the Legitimate and the Diffusion of Legitimacy.” Sociological Science 1: 49–69.
  • Received: September 17, 2013
  • Accepted: September 20, 2013
  • Editors: Jesper Sørensen, Ezra Zuckerman
  • DOI: 10.15195/v1.a5

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