JOURNAL ARTICLE

A New Take on the Categorical Imperative: Gatekeeping, Boundary Maintenance, and Evaluation Penalties in Science.

  • Published In: Organization Science (INFORMS), 2023, v. 34, n. 3. P. 1090 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Fini, Riccardo; Jourdan, Julien; Perkmann, Markus; Toschi, Laura 3 of 3

Abstract

This article examines the valuation penalty faced by candidates with unfocused identities—specifically multidisciplinary academics—in the context of academic accreditation, where evaluators act as gatekeepers to social entities such as scientific disciplines. Using data from the 2012 national scientific qualification in Italian academia, the study finds that high-performing multidisciplinary candidates incur a stronger penalty compared to lower-performing ones, suggesting that evaluators reject them not due to confusion or doubts about ability but because these candidates threaten the distinctiveness and boundaries of the discipline. The penalty is more pronounced in smaller and more distinctive disciplines and when evaluation panels are composed of highly typical disciplinary members. The findings propose that beyond cognitive or quality-assessment explanations, boundary maintenance—efforts by incumbents to preserve social and knowledge domain boundaries—plays a critical role in social valuation during accreditation, with implications for managing conservatism and innovation in professional gatekeeping processes.

Additional Information

  • Source:Organization Science (INFORMS). 2023/05, Vol. 34, Issue 3, p1090
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:1047-7039
  • DOI:10.1287/orsc.2022.1610
  • Accession Number:163655244
  • Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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