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Segregated in Social Space: The Spatial Structure of Acquaintanceship Networks

By on November 29, 2021 in Articles

Barum Park

Sociological Science November 29, 2021
10.15195/v8.a20

Abstract

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With deepening cleavages on several social dimensions, social fragmentation has become a major concern across the social sciences. This article proposes a spatial approach to study the segregation pattern of acquaintanceship ties across multiple social dimensions simultaneously. A Bayesian unfolding model is developed and fitted to the 2006 General Social Survey. Results suggests that the segregation pattern of reported acquaintanceship ties reflect consolidated socioeconomic inequalities. Furthermore, among the 13 analyzed social groups, gay and lesbian people were the least segregated group in 2006, implying that individuals with very different network compositions had similar probabilities to know someone who is gay or lesbian. Lastly, contradicting previous findings that ideology and religiosity segregate acquaintanceship networks to an extent that rivals race, it is found that race stands out as the dominant dimension that shapes the distribution of these relationships.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Barum Park: Department of Sociology, Cornell University
E-mail: b.park@cornell.edu

Acknowledgments: I am grateful to Delia Baldassarri, Siwei Cheng, Ned Crowley, Paul DiMaggio, Mike Hout, Harris Hyun-Soo Kim, Byungkyu Lee, Jeff Manza, John Levi Martin, Oscar Stuhler, and LarryWu for their invaluable comments. This article was presented in the Inequality Workshop at New York University, the Annual Conference of the Association of Korean Sociologists in America in 2018, and the American Sociological Association Annual Meeting in 2018. All remaining errors are my own.

Supplemental Material

  • Citation: Park, Barum. 2021. “Segregated in Social Space: The Spatial Structure of Acquaintanceship Networks.” Sociological Science 8: 397-428.
  • Received: August 15, 2019
  • Accepted: October 3, 2021
  • Editors: Jesper Sørensen, Olav Sorenson
  • DOI: 10.15195/v8.a20


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Social Distance and Knowledge Transformation: The Effects of Social Network Distance on Organizational Learning

By on December 15, 2016 in Articles

Brandy Aven, Evelyn Ying Zhang

Sociological Science, December 15, 2016
DOI 10.15195/v3.a48

Abstract
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Organizations are increasingly adopting technologies to promote knowledge sharing across boundaries of specialized groups. Yet, prior research beginning with March (1991) suggests that such knowledge-sharing technologies actually inhibit organizational learning by reducing solution diversity. This line of reasoning stems from the prior literature’s assumption that the knowledge shared will be transferred from one member to another perfectly and without distortion. We challenge this assumption and argue, instead, that knowledge is often altered or transformed when it is shared between members and that the degree of this transformation increases as the social distance between the knowledge sender and receiver increases. Because the implementation of knowledge-sharing technologies encourages learning between members across greater social distances, it increases knowledge transformation. Thus, knowledge-sharing technologies present a new opportunity to diversify solutions and lead to innovation.

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

Brandy Aven: Tepper School of Business, Carnegie Mellon University
Email: aven@cmu.edu

Evelyn Ying Zhang: Tepper School of Business, Carnegie Mellon University
Email: yingzhang@cmu.edu

Acknowledgements: We are particularly grateful to Linda Argote for her extensive comments and suggestions. We also wish to thank all of the participants in the Strategy and Management Seminar Series at the Tuck School of Business, the Organizational Behavior and Theory Seminar at the Tepper School of Business, and the Carnegie School’s Organizational Learning Conference for the helpful suggestions and critiques. Please direct correspondence to Brandy Aven, Tepper School of Business, Carnegie Mellon University.


  • Citation: Aven, Brandy, and Evelyn Ying Zhang. 2016. “Social Distance and Knowledge Transformation: The Effects of Social Network Distance on Organizational Learning.” Sociological Science 3: 1103-1131.
  • Received: September 8, 2016
  • Accepted: October 28, 2016
  • Editors: Jesper Sørensen, Olav Sorenson
  • DOI: 10.15195/v3.a48


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New Dimensions of Tolerance: A Case for a Broader, Categorical Approach

By on November 24, 2014 in Articles

Darin M. Mather, Eric Tranby

Sociological Science, November 24, 2014
DOI 10.15195/v1.a28

Abstract
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Most social scientific research on tolerance rests upon two assumptions: 1) that tolerance is mainly concerned with the extension of political rights, and 2) that the concept is best understood as a unidimensional continuum of attitudes that are more or less tolerant. We argue that to have a fuller understanding of tolerance, we must transcend these two assumptions to develop a concept that is multidimensional. We use latent class analysis and confirmatory factor analysis to uncover new patterns of tolerant responses to least liked groups. Our results reveal four different profiles, which describe four different approaches to objectionable groups. These are generally intolerant, politically tolerant, generally tolerant and privately tolerant. Our profiles provide a fuller, more nuanced description of tolerant and intolerant attitudes than traditional approaches. These profiles can be used to help social scientists refine existing theories on the mechanisms of tolerance.
Darin M. Mather: Crown College E-mail: matherd@crown.edu

Eric Tranby: University of Delaware  E-mail: etranby@edel.edu

Supplemental Material

  • Citation: Mather, Darin M. and Eric Tranby. 2014. “New Dimensions of Tolerance: A Case for a Broader, Categorical Approach.” Sociological Science 1:512-531.
  • Received: March 10, 2014
  • Accepted: July 24, 2014
  • Editors: Jesper Sørensen,  Kim Weeden
  • DOI: 10.15195/v1.a28

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