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      Beyond the ivory tower: Measuring and explaining academic engagement with journalists, politicians and industry representatives among Swiss professorss

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          Abstract

          Scholars from different theoretical schools have posited that in recent decades, science and society have moved closer together, and the concept of academic engagement has been proposed to capture one part of this approximation empirically. This study analyzes the academic engagement of individual scholars towards politicians, industry representatives and journalists. It uses comprehensive survey data on Swiss professors from all disciplines, all the country’s universities and from associated research institutes. It assesses, firstly, the degree to which these professors have professional contacts to journalists, politicians and industry representatives. Secondly, it explains the extent of these contacts, using multi-level modelling that incorporates individual factors as well as organizational and institutional contexts. Our study shows that academic engagement is quite common with strong differences between disciplines. Furthermore, professors with higher academic productivity, positive personal attitude towards communication activities as well as a leadership position have more outside contacts. The gender and nationality of the professors, however, only play a role for some of the contacts with non-scientific actors.

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              Stan: A Probabilistic Programming Language

              Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: 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
                21 May 2021
                2021
                : 16
                : 5
                : e0251051
                Affiliations
                [1 ] Graduate Institute of Journalism, National Taiwan University, Taipei, Taiwan, R.O.C
                [2 ] Department of Communication and Media Research, University of Zurich, Zurich, Switzerland
                Universidad de las Palmas de Gran Canaria, SPAIN
                Author notes

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

                Author information
                https://orcid.org/0000-0003-1232-083X
                https://orcid.org/0000-0002-4454-5999
                Article
                PONE-D-20-01506
                10.1371/journal.pone.0251051
                8139464
                34019575
                22d029f4-8706-4339-92ac-1d8e8c7cf050
                © 2021 Rauchfleisch 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
                : 23 January 2020
                : 20 April 2021
                Page count
                Figures: 3, Tables: 2, Pages: 20
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Science Policy
                Science and Technology Workforce
                Careers in Research
                Scientists
                People and Places
                Population Groupings
                Professions
                Scientists
                Social Sciences
                Economics
                Industrial Organization
                Social Sciences
                Sociology
                Education
                Schools
                Universities
                Biology and Life Sciences
                Psychology
                Psychological Attitudes
                Social Sciences
                Psychology
                Psychological Attitudes
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Science Policy
                Science and Technology Workforce
                Careers in Research
                People and Places
                Geographical Locations
                Europe
                Switzerland
                Custom metadata
                The raw data cannot be shared publicly because it would allow to identify unique professors. However, we will make an anonymized data set that ensures the anonymity of individual participants used for our analysis available on Harvard Dataverse for academic researchers: https://doi.org/10.7910/DVN/WUWIZS.

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