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      Diversity of artists in major U.S. museums

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          Abstract

          The U.S. art museum sector is grappling with diversity. While previous work has investigated the demographic diversity of museum staffs and visitors, the diversity of artists in their collections has remained unreported. We conduct the first large-scale study of artist diversity in museums. By scraping the public online catalogs of 18 major U.S. museums, deploying a sample of 10,000 artist records comprising over 9,000 unique artists to crowdsourcing, and analyzing 45,000 responses, we infer artist genders, ethnicities, geographic origins, and birth decades. Our results are threefold. First, we provide estimates of gender and ethnic diversity at each museum, and overall, we find that 85% of artists are white and 87% are men. Second, we identify museums that are outliers, having significantly higher or lower representation of certain demographic groups than the rest of the pool. Third, we find that the relationship between museum collection mission and artist diversity is weak, suggesting that a museum wishing to increase diversity might do so without changing its emphases on specific time periods and regions. Our methodology can be used to broadly and efficiently assess diversity in other fields.

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          A reliability analysis of Mechanical Turk data

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            Gender disparities in colloquium speakers at top universities

            Recently, research has focused on identifying gender gatekeepers—people or practices that may (unintentionally) engage in, create, or maintain gender disparities. In the current research, we examine gender differences in academic colloquium speakers. Colloquium talks lead to enhancement of a researcher’s reputation, networks, research collaborations, and sometimes result in job offers. Results from our three studies indicate that women are underrepresented relative to men as colloquium speakers across six disciplines. To examine the role of self-selection, we find that women neither decline talk invitations at greater rates nor question the importance of talks more than men do. Finally, we show that the presence of women as colloquium chairs (and potentially committee members) increases the likelihood of having female colloquium speakers. Colloquium talks at prestigious universities both create and reflect academic researchers’ reputations. Gender disparities in colloquium talks can arise through a variety of mechanisms. The current study examines gender differences in colloquium speakers at 50 prestigious US colleges and universities in 2013–2014. Using archival data, we analyzed 3,652 talks in six academic disciplines. Men were more likely than women to be colloquium speakers even after controlling for the gender and rank of the available speakers. Eliminating alternative explanations (e.g., women declining invitations more often than men), our follow-up data revealed that female and male faculty at top universities reported no differences in the extent to which they ( i ) valued and ( ii ) turned down speaking engagements. Additional data revealed that the presence of women as colloquium chairs (and potentially on colloquium committees) increased the likelihood of women appearing as colloquium speakers. Our data suggest that those who invite and schedule speakers serve as gender gatekeepers with the power to create or reduce gender differences in academic reputations.
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              Gender Representation on Journal Editorial Boards in the Mathematical Sciences

              We study gender representation on the editorial boards of 435 journals in the mathematical sciences. Women are known to comprise approximately 15% of tenure-stream faculty positions in doctoral-granting mathematical sciences departments in the United States. Compared to this pool, the likely source of journal editorships, we find that 8.9% of the 13067 editorships in our study are held by women. We describe group variations within the editorships by identifying specific journals, subfields, publishers, and countries that significantly exceed or fall short of this average. To enable our study, we develop a semi-automated method for inferring gender that has an estimated accuracy of 97.5%. Our findings provide the first measure of gender distribution on editorial boards in the mathematical sciences, offer insights that suggest future studies in the mathematical sciences, and introduce new methods that enable large-scale studies of gender distribution in other fields.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2019
                20 March 2019
                : 14
                : 3
                : e0212852
                Affiliations
                [1 ] Department of Mathematics and Statistics, Williams College, Williamstown, MA, United States of America
                [2 ] Graduate Program in Data Science, New College of Florida, Sarasota, FL, United States of America
                [3 ] Department of Mathematics, Statistics, and Computer Science, Macalester College, St. Paul, MN, United States of America
                [4 ] Department of Art, Williams College, Williamstown, MA, United States of America
                [5 ] Department of Art History, University of California Los Angeles, Los Angeles, CA, United States of America
                [6 ] Williams College Museum of Art, Williamstown, MA, United States of America
                University of Vermont, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9977-3414
                http://orcid.org/0000-0002-1453-1908
                Article
                PONE-D-18-35210
                10.1371/journal.pone.0212852
                6426178
                30893328
                4bf3c26a-7c9b-4142-af8f-055f06f6475e
                © 2019 Topaz et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 13 December 2018
                : 11 February 2019
                Page count
                Figures: 2, Tables: 3, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100005972, Williams College;
                Award Recipient :
                CT was supported by funding from the Williams College Office of the Dean of Faculty, Science Division, Davis Center, and Department of Mathematics and Statistics. The funders had no rule in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Research and Analysis Methods
                Research Facilities
                Museum Collections
                People and Places
                Population Groupings
                Ethnicities
                Research and Analysis Methods
                Research Design
                Survey Research
                Census
                Earth Sciences
                Geography
                Regional Geography
                Computer and Information Sciences
                Computer Networks
                Internet
                People and places
                Geographical locations
                North America
                Research and Analysis Methods
                Research Design
                Survey Research
                Surveys
                Computer and Information Sciences
                Geoinformatics
                Earth Sciences
                Geography
                Geoinformatics
                Custom metadata
                Data are available and may be explored interactively at https://artofstat.shinyapps.io/ArtistDiversity. The code used for analysis is available in a GitHub repository at https://github.com/artofstat/ArtistDiversity.

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                Uncategorized

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