Strategic Ambiguity: How Pre-Tenure Faculty Negotiate the Hidden Rules of Academia.
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| Title: | Strategic Ambiguity: How Pre-Tenure Faculty Negotiate the Hidden Rules of Academia. |
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| Authors: | Cate, Leandra1 lmc64@psu.edu, Ward, LaWanda W. M.1, Ford, Karly S.1 |
| Source: | Innovative Higher Education. Oct2022, Vol. 47 Issue 5, p795-812. 18p. |
| Subject Terms: | *Tenure of college teachers, *Teachers, *Higher education, Ambiguity, Racialization |
| Abstract: | The tenure evaluation process is characterized by a lack of clarity and governed by unspoken rules. At the same time, while institutions have increased the presence of racially minoritized people among the ranks of faculty over the last 30 years, this growth in numbers has been concentrated among non-tenure track and pre-tenure levels. This study analyzes the ways that ambiguity in the tenure evaluation process contributes to the racialized hierarchy of the professoriate. Framed by theories of strategic ambiguity and racialized organizations, we interviewed 30 pre-tenure faculty at a research-intensive university. Findings reveal that faculty in our study relate to the ambiguity of tenure evaluation primarily in one of two ways: 1) with ambivalent acceptance or 2) with a critical understanding of ambiguity as strategic, benefitting the institution. These relationships to ambiguity were differentiated by race, with White faculty describing ambivalence and racially minoritized faculty critiquing ambiguity as strategic and inequitable. Finally, we found some evidence that some White pre-tenure faculty found paths to clarity through racial privilege. Implications for research and practice include a clearer understanding of the ways ambiguity in higher education is strategic and racialized. [ABSTRACT FROM AUTHOR] |
| Copyright of Innovative Higher Education is the property of Springer Nature 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.) | |
| Database: | Education Research Complete |
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