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      Age and Gender Representation on German TV : A Longitudinal Computational Analysis

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          Abstract

          Television offers an enticing glimpse into the world, but its perspective is often skewed. When societal groups are systematically excluded from appearing on the screen, they lose the chance to represent their characteristics and interests. Recipients may then form distorted perceptions and attitudes towards those groups. Empirical research on the prevalence of such biases - especially across stations, time, and genre - has been limited by the effort of manual content analyses. We develop and validate a deep-learning based method for measuring age and gender of faces in video material. An analysis of approximately 16 million faces from six years of German mainstream TV across six stations is fused with existing program metadata indicating timing and genre of broadcasts, including advertisements. Multilevel regression models show a consistent and temporally stable discrimination against women and elderly people, along with a double discrimination of elderly women. A significant amount of variation across genres and systematic differences between public and private broadcasters furthermore indicate previously undocumented heterogeneity in the representation of societal groups on TV. We discuss potential implications of a genre-specific differentiation against the backdrop of societal trends.

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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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            Robust Real-Time Face Detection

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              Deformable Convolutional Networks

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                Author and article information

                Contributors
                Journal
                CCR
                Computational Communication Research
                Amsterdam University Press (Amsterdam )
                2665-9085
                2665-9085
                February 2022
                : 4
                : 1
                Affiliations
                University of Mainz
                University of Mainz
                University of Mainz
                Article
                CCR2022.1.005.JURG
                10.5117/CCR2022.1.005.JURG
                67c6d691-88b4-490d-9849-5aceb996ffe9
                © Pascal Jürgens, Christine E. Meltzer & Michael Scharkow

                This is an open access article distributed under the terms of the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/

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                age representation,deep learning,television,gender representation,image analysis

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