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      Engaging the public or asking your friends? Analysing science-related crowdfunding using behavioural and survey data

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          Abstract

          Science-related crowdfunding enables public engagement with science. However, we know little about citizens engaging with science this way: Who are the people engaging with and donating to science through crowdfunding – and how do they decide how much to give? This study analyses behavioural and survey data from the Swiss crowdfunding platform wemakeit ( N = 576). Results illustrate that a small, non-representative segment of the public engages with science through crowdfunding. Compared to the general public in Switzerland, these backers have an above-average education and income. Science-related crowdfunding mainly reaches citizens with an existing interest in science, personal ties to project initiators or the scientific community. The size of backers’ donations correlates with perceived personal appeals in campaigns or connections to initiators rather than projects’ scientific merit. While science-related crowdfunding thus opens up new avenues for public outreach by the scientific community, its potential for broader public engagement with science seems limited.

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          Most cited references45

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          Fitting Linear Mixed-Effects Models Using lme4

          Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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            Signaling in Equity Crowdfunding

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              Public engagement as a means of restoring public trust in science--hitting the notes, but missing the music?

              This paper analyses the recent widespread moves to 'restore' public trust in science by developing an avowedly two-way, public dialogue with science initiatives. Noting how previously discredited and supposedly abandoned public deficit explanations of 'mistrust' have actually been continually reinvented, it argues that this is a symptom of a continuing failure of scientific and policy institutions to place their own science-policy institutional culture into the frame of dialogue, as possible contributory cause of the public mistrust problem. Copyright 2006 S. Karger AG, Basel.
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                Author and article information

                Contributors
                Journal
                Public Underst Sci
                Public Underst Sci
                PUS
                sppus
                Public Understanding of Science (Bristol, England)
                SAGE Publications (Sage UK: London, England )
                0963-6625
                1361-6609
                2 August 2022
                November 2022
                : 31
                : 8
                : 993-1011
                Affiliations
                [1-09636625221113134]Ludwig-Maximilians-Universität München, Germany
                [2-09636625221113134]University of Zurich, Switzerland
                [3-09636625221113134]University of Münster, Germany
                [4-09636625221113134]Ecole polytechnique fédérale de Lausanne, Switzerland
                Author notes
                [*]Valerie Hase, Department of Media and Communication, Ludwig-Maximilians-Universität München, Akademiestr. 7, 80799 Munich, Germany. Email: Valerie.hase@ 123456ifkw.lmu.de
                Author information
                https://orcid.org/0000-0001-6656-4894
                https://orcid.org/0000-0002-0847-7503
                https://orcid.org/0000-0003-4328-6419
                https://orcid.org/0000-0001-9653-7299
                Article
                10.1177_09636625221113134
                10.1177/09636625221113134
                9630956
                35916455
                7f2d34c4-1ba1-4f7c-a7d8-9f384ac4dd5b
                © The Author(s) 2022

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Funding
                Funded by: Gebert Rüf Stiftung, FundRef https://doi.org/10.13039/501100003357;
                Award ID: GRS-040/15
                Funded by: Gebert Rüf Stiftung, FundRef https://doi.org/10.13039/501100003357;
                Award ID: GRS–015/18
                Categories
                Articles
                Custom metadata
                ts1

                Sociology
                crowdfunding,digital platforms,funding,public engagement,science communication
                Sociology
                crowdfunding, digital platforms, funding, public engagement, science communication

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