International audienceThe issue of the influence of norms on behavior is as old as sociology itself. This paper explores the effect of normative homophily (i.e. “sharing the same normative choices”) on the evolution of the advice network among lay judges in a courthouse. 0020 and 0025 social exchange theory suggests that members select advisors based on the status of the advisor. Additional research shows that members of an organization use similarities with others in ascribed, achieved or inherited characteristics, as well as other kinds of ties, to mitigate the potentially negative effects of this strong status rule. We elaborate and test these theories using data on advisor choice in the Commercial Court of Paris. We use a jurisprudential case about unfair competition (material and “moral” damages), a case that we submitted to all the judges of this court, to test the effect of normative homophily on the selection of advisors, controlling for status effects. Normative homophily is measured by the extent to which two judges are equally “punitive” in awarding damages to plaintiffs. Statistical analyses combine longitudinal advice network data collected among the judges with their normative dispositions. Contrary to what could be expected from conventional sociological theories, we find no pure effect of normative homophily on the choice of advisors. In this case, therefore, sharing the same norms and values does not have, by itself, a mitigating effect and does not contribute to the evolution of the network. We argue that status effects, conformity and alignments on positions of opinion leaders in controversies still provide the best insights into the relationship between norms, structure and behavior
International audienceThe paper investigates the place of visual tools in mixed-methods research on social networks, arguing that they can not only improve the communicability of results, but also support research at the data gathering and analysis stages. Three examples from the authors' own research experience illustrate how sociograms can be integrated in multiple ways with other analytical tools, both quantitative and qualitative, positioning visualization at the intersection of varied methods and channelling substantive ideas as well as network insight in a coherent way. Visualization also facilitates the participation of a broad range of stakeholders, including among others, study participants and non-specialist researchers. It can support the capacity of qualitative and mixed-methods research to reach out to areas of the social that are difficult to circumscribe, such as hidden populations and informal organisations. On this basis, visualization appears as a unique opportunity for mixing methods in the study of social networks, emphasizing both structure and process at the same time
2020 marks the 25th anniversary of the “digital divide.” Although a quarter century has passed, legacy digital inequalities continue, and emergent digital inequalities are proliferating. Many of the initial schisms identified in 1995 are still relevant today. Twenty-five years later, foundational access inequalities continue to separate the digital haves and the digital have-nots within and across countries. In addition, even ubiquitous-access populations are riven with skill inequalities and differentiated usage. Indeed, legacy digital inequalities persist vis-à-vis economic class, gender, sexuality, race and ethnicity, aging, disability, healthcare, education, rural residency, networks, and global geographies. At the same time, emergent forms of inequality now appear alongside legacy inequalities such that notions of digital inequalities must be continually expanded to become more nuanced. We capture the increasingly complex and interrelated nature of digital inequalities by introducing the concept of the “digital inequality stack.” The concept of the digital inequality stack encompasses access to connectivity networks, devices, and software, as well as collective access to network infrastructure. Other layers of the digital inequality stack include differentiated use and consumption, literacies and skills, production and programming, etc. When inequality exists at foundational layers of the digital inequality stack, this often translates into inequalities at higher levels. As we show across these many thematic foci, layers in the digital inequality stack may move in tandem with one another such that all layers of the digital inequality stack reinforce disadvantage.
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