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Gatekeepers to the community college presidency for leaders of color.

  • Published In: New Directions for Community Colleges, 2023, v. 2023, n. 202. P. 47 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hines, CharMaine Y. 3 of 3

Abstract

Research has identified a clear underrepresentation of race and gender diversity in the community college presidency and scant progress in diversification. This phenomenological study used critical race theory (CRT) and glass ceiling theory (GCT) lenses to examine the lived experiences of minority community college presidents, including those who identified as African American, Asian Pacific Islander, and Latino/Hispanic and spanned 12 states and every region of the U.S. The counter‐narratives of the participants were examined using a modified interpretative phenomenological approach concept model with cultural domain analysis (CDA) for validation. Participants were categorized by generational definitions and their associated community college development leadership styles. Findings include evidence of a leaky pipeline, a flawed hiring process with gatekeepers along the hiring continuum, and biases and stereotypes encountered, illustrating leaders' perceptions of underrepresentation of minorities serving in presidential roles in community colleges. The study identified numerous deficiencies impacting this underrepresentation and offered recommendations to improve practice. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:New Directions for Community Colleges. 2023/06, Vol. 2023, Issue 202, p47
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:0194-3081
  • DOI:10.1002/cc.20568
  • Accession Number:164586576
  • Copyright Statement:Copyright of New Directions for Community Colleges 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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