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      Look Who's Talking : Gender Differences in Academic Job Talks

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            Abstract

            The "job talk"is a standard element of faculty recruiting. How audiences treat candidates for faculty positions during job talks could have disparate impact on protected groups, including women. We annotated 156 job talks from five engineering and science departments for 13 categories of questions and comments. All departments were ranked in the top 10 by US News & World Report. We find that differences in the number, nature, and total duration of audience questions and comments are neither material nor statistically significant. For instance, the median difference (by gender) in the duration of questioning ranges from zero to less than two minutes in the five departments. Moreover, in some departments, candidates who were interrupted more often were more likely to be offered a position, challenging the premise that interruptions are necessarily prejudicial. These results are specific to the departments and years covered by the data, but they are broadly consistent with previous research, which found differences of comparable in magnitude. However, those studies concluded that the (small) differences were statistically significant. We present evidence that the nominal statistical significance is an artifact of using inappropriate hypothesis tests. We show that it is possible to calibrate those tests to obtain a proper P-value using randomization.

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            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            21 March 2023
            Affiliations
            [1 ] Department of Statistics, University of California, Berkeley;
            [2 ] Department of Electrical Engineering and Computer Science, University of California, Berkeley;
            [3 ] Department of Statistics, Carnegie Mellon University;
            [4 ] Department of Statistics, University of California, Los Angeles;
            [5 ] Department of Statistics, University of Washington;
            [6 ] Department of Statistics, Stanford University;
            [7 ] Department of Mechanical Engineering, University of California, Berkeley;
            Author notes
            Author information
            https://orcid.org/0000-0002-3229-7924
            Article
            10.14293/PR2199.000025.v2
            ae90a53b-9110-41a1-9279-28df4c45dce6

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 20 March 2023
            Categories

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Applications,Statistics
            job talk,permutation,academia,randomization tests,nonparametric,type III error,gender

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