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      Systematic inequality and hierarchy in faculty hiring networks

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          Abstract

          An analysis of networks of graduate-to-faculty hires reveals systematic hiring biases and patterns.

          Abstract

          The faculty job market plays a fundamental role in shaping research priorities, educational outcomes, and career trajectories among scientists and institutions. However, a quantitative understanding of faculty hiring as a system is lacking. Using a simple technique to extract the institutional prestige ranking that best explains an observed faculty hiring network—who hires whose graduates as faculty—we present and analyze comprehensive placement data on nearly 19,000 regular faculty in three disparate disciplines. Across disciplines, we find that faculty hiring follows a common and steeply hierarchical structure that reflects profound social inequality. Furthermore, doctoral prestige alone better predicts ultimate placement than a U.S. News & World Report rank, women generally place worse than men, and increased institutional prestige leads to increased faculty production, better faculty placement, and a more influential position within the discipline. These results advance our ability to quantify the influence of prestige in academia and shed new light on the academic system.

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          Most cited references28

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Understanding current causes of women's underrepresentation in science

            Explanations for women's underrepresentation in math-intensive fields of science often focus on sex discrimination in grant and manuscript reviewing, interviewing, and hiring. Claims that women scientists suffer discrimination in these arenas rest on a set of studies undergirding policies and programs aimed at remediation. More recent and robust empiricism, however, fails to support assertions of discrimination in these domains. To better understand women's underrepresentation in math-intensive fields and its causes, we reprise claims of discrimination and their evidentiary bases. Based on a review of the past 20 y of data, we suggest that some of these claims are no longer valid and, if uncritically accepted as current causes of women's lack of progress, can delay or prevent understanding of contemporary determinants of women's underrepresentation. We conclude that differential gendered outcomes in the real world result from differences in resources attributable to choices, whether free or constrained, and that such choices could be influenced and better informed through education if resources were so directed. Thus, the ongoing focus on sex discrimination in reviewing, interviewing, and hiring represents costly, misplaced effort: Society is engaged in the present in solving problems of the past, rather than in addressing meaningful limitations deterring women's participation in science, technology, engineering, and mathematics careers today. Addressing today's causes of underrepresentation requires focusing on education and policy changes that will make institutions responsive to differing biological realities of the sexes. Finally, we suggest potential avenues of intervention to increase gender fairness that accord with current, as opposed to historical, findings.
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              An Evolutionary Approach to Norms

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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                February 2015
                12 February 2015
                : 1
                : 1
                : e1400005
                Affiliations
                [1 ]Department of Computer Science, University of Colorado, Boulder, CO 80309, USA.
                [2 ]BioFrontiers Institute, University of Colorado, Boulder, CO 80303, USA.
                [3 ]Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA.
                [4 ]Ewing Marion Kauffman Foundation, Kansas City, MO 64110, USA.
                [5 ]Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
                [6 ]Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA 02115, USA.
                Author notes
                [* ]Corresponding author. E-mail: aaron.clauset@ 123456colorado.edu
                Article
                1400005
                10.1126/sciadv.1400005
                4644075
                26601125
                638bcc33-e79d-47ab-b24b-260222b7fca6
                Copyright © 2015, The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 22 September 2014
                : 21 December 2014
                Funding
                Funded by: Ewing Marion Kauffman Foundation;
                Award ID: 20120085
                Award Recipient :
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Social Sciences
                Network Sciences

                faculty placement,hiring networks,prestige,inequality,hierarchy

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