JOURNAL ARTICLE

'The Machine Takes Our Jobs Away': The problem of technological unemployment in the work of Chicago sociologist William F. Ogburn.

  • Published In: Journal of the History of the Behavioral Sciences, 2023, v. 59, n. 4. P. 363 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kim, Emy; Solovey, Mark 3 of 3

Abstract

This paper examines the Chicago sociologist William F. Ogburn's (1886–1959) views about technological unemployment, which were intimately connected to his analysis of the social impacts of technological developments and resulting social problems due to cultural lag. We trace the development of his views as seen through his well‐known 1922 book, Social Change with Respect to Culture and Original Nature, his important contributions to the President's Research Committee on Social Trends (1933), and his lesser‐known pamphlets designed for a broader audience—Living with Machines (1933), You and Machines (1934), and Machines and Tomorrow's World (1938). He used these pamphlets to educate the public about the dangers of new machines and technological unemployment. In doing so, he drew upon sociological analysis in his professional scholarly writings and his long‐standing personal interests in social betterment and social reform. Our analysis also calls into question the adequacy of existing scholarship on Ogburn that has emphasized his commitment to a statistical, dispassionate, and "objectivist" approach to social science research. We call for a revised, richer, and more complex view of Ogburn's work and legacy as one of the nation's leading social scientists during the first half of the 20th century. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of the History of the Behavioral Sciences. 2023/10, Vol. 59, Issue 4, p363
  • Document Type:Article
  • Subject Area:Sociology
  • Publication Date:2023
  • ISSN:0022-5061
  • DOI:10.1002/jhbs.22242
  • Accession Number:173340185
  • Copyright Statement:Copyright of Journal of the History of the Behavioral Sciences is the property of Wiley-Blackwell 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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