JOURNAL ARTICLE

It's the Union Leaders, Stupid: Organized Labor's Failures in the South.

  • Published In: Reviews in American History, 2023, v. 51, n. 2. P. 152 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Pearson, Chad 3 of 3

Abstract

These historical actors and their scholarly champions suffer from what Goldfield aptly calls "the poverty of U.S. liberalism" (p. 34).[4] Liberal labor heads, many of whom were embedded in the political establishment, were exceptionally contemptuous of the Communist Party (CP), insisting that the organization did more harm than good. While many liberal scholars have described the New Deal state in a progressive light, noting its limited workplace-based protections and support for collective bargaining, Goldfield, an extraordinarily well-read Marxist, points out that the strongest unions, including the United Mine Workers, secured recognition from their stubborn bosses I before i the establishment of Section 7(a) of the National Industrial Recovery Act. Goldfield's point is critical, noting "the shallowness of those who have argued that it was the New Deal that was responsible for union organizing successes during the 1930s, rather than the workers themselves and their organizations" (p. 282). CP union activists were successful partially because of their commitment to racial equality, and Goldfield spotlights the importance of interracial solidarity and the Communist-aligned National Negro Congress (p. 138). [Extracted from the article]

Additional Information

  • Source:Reviews in American History. 2023/06, Vol. 51, Issue 2, p152
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2023
  • ISSN:0048-7511
  • DOI:10.1353/rah.2023.a911211
  • Accession Number:173420338
  • Copyright Statement:Copyright of Reviews in American History is the property of Johns Hopkins University Press 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.