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Networked Inequality: Studies on Diversity and Marginalization


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Systemic inequality makes life disproportionately challenging for disadvantaged people and groups. Such inequalities are pervasive across societies and institutions and are perpetuated within networks. Address­ing systemic inequality depends, at least in part, on effectively modeling it, describing its antecedents and outcomes, and identifying effective network interventions. Bringing a networked lens to the study of inequality has profound implications for the improvement of societal and individual well-being, and could lead to more effective access to education, reductions in income inequality, changing relationships to power, and/or stemming the deleterious effects of discrimination, to name just a few. Thanks to the availability of rich data on human interaction, approaches from network science and data science can be applied to examine the complex processes that produce and perpetuate inequality.

There are many ways that network science can be used to reduce inequality, including (but not limited to): identifying at-risk people and communities; highlighting opportunities for network interventions; interrogating power imbalances; revealing identity-based biases and under representation across a variety of domains (human mobility, public health, career success, political influence, scientific collaboration, etc.) This special issue aims to attract innovative original research that applies network science to study human inequality. The basis for inequality may be gender, race, age, sexual orientation, socioeconomic status, ability, and/or any other factors that lead to marginalization. The importance of such studies has been recognized by the United Nations, where at least two Sustainable Development Goals are related to the special issue: SDG5 (Gender Equality) and SDG10 (Reduction of Inequalities).

Lead Guest Editors

Brooke Foucault Welles - b.welles@northeastern.edu 
Department of Communication Studies, Northeastern University, USA

Olga Sarmiento - osarmien@uniandes.edu.co
School of Medicine, Universidad de Los Andes, Colombia

Guest Editors

Ana Maria Jaramillo - aj499@exeter.ac.uk 
BioComplex Laboratory, Department of Computer Science, University of Exeter, UK

Mariana Macedo - mg615@exeter.ac.uk
BioComplex Laboratory, Department of Computer Science, University of Exeter, UK


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