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      Hide and seek: The connection between false beliefs and perceptions of government transparency

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          Abstract

          This research examines how false beliefs shape perceptions of government transparency in times of crisis. Measuring transparency perceptions using both closed- and open-ended questions drawn from a Canadian panel survey, we show that individuals holding false beliefs about COVID-19 are more likely to have negative perceptions of government transparency. They also tend to rely on their false beliefs when asked to justify why they think governments are not being transparent about the pandemic. Our findings suggest that the inability to successfully debunk misinformation could worsen perceptions of government transparency, further eroding political support and contributing to non-compliance with public health directives.

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          Most cited references53

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          The spread of true and false news online

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            The case for motivated reasoning.

            Ziva Kunda (1990)
            It is proposed that motivation may affect reasoning through reliance on a biased set of cognitive processes--that is, strategies for accessing, constructing, and evaluating beliefs. The motivation to be accurate enhances use of those beliefs and strategies that are considered most appropriate, whereas the motivation to arrive at particular conclusions enhances use of those that are considered most likely to yield the desired conclusion. There is considerable evidence that people are more likely to arrive at conclusions that they want to arrive at, but their ability to do so is constrained by their ability to construct seemingly reasonable justifications for these conclusions. These ideas can account for a wide variety of research concerned with motivated reasoning.
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              Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

              This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.
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                Author and article information

                Journal
                Harvard Kennedy School Misinformation Review
                HKS Misinfo Review
                Shorenstein Center for Media, Politics, and Public Policy
                March 16 2022
                March 16 2022
                Affiliations
                [1 ]Department of Political Science, McGill University, Canada,
                [2 ]Department of Political Science, Université de Montréal, Canada
                [3 ]School of Social and Political Science, University of Edinburgh, United Kingdom
                Article
                10.37016/mr-2020-90
                510b5757-0754-435a-a318-917a17f37ab5
                © 2022
                History

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