An official website of the United States government
Academic AbstractAdvantaged group allies have multiple motives for supporting equality, raising questions about their sincerity. We draw upon the covariation model of attributions to explain how disadvantaged group members make attributions about whether advantaged group “allies” are sincerely motivated to empower the disadvantaged group. We propose an Attribution-Identity Model of Sincerity (AIMS) which posits that disadvantaged group members view advantaged group members as sincere allies when they support equality in the presence of inhibitory causes and in the absence of facilitative causes, exceed expectations for the advantaged group, and provide support across time and contexts. Furthermore, those who identify strongly with their disadvantaged group and perceive intergroup inequality as illegitimate are most motivated to ascertain the sincerity of advantaged group members’ allyship. AIMS suggests how members of disadvantaged groups seek to maximize benefits and minimize risks of advantaged group members’ allyship. Public AbstractAdvantaged group members (e.g., men, White Americans) can act as allies for disadvantaged groups (e.g., women, Americans belonging to minoritized racial groups), but members of disadvantaged groups sometimes have reason to question whether their motives are sincere. We argue that members of disadvantaged groups view advantaged group allies as more sincere when they support equality when they do not stand to benefit from it and even when they stand to lose. We also argue that members of disadvantaged groups view advantaged group allies as more sincere when their support for equality goes beyond expectations for their advantaged group, consistently over time, and is not limited to particular situations, forms, or contexts. Members of disadvantaged groups like sincere allies, want to work with them, and feel safe around them. Sincere allies also serve as moral exemplars to other members of advantaged groups.
more »
« less
Surveillance Working Groups as Geomedia Governance
Municipalities across the US are investing in smart technologies that rely on data collection tools and devices. Though proposals to procure these technologies often describe the benefits of optimization, privacy concerns and asymmetrical data access remain. Some municipalities are working to minimize such concerns by developing community working groups to evaluate the adoption of surveillance technologies. Many of these organizations have an explicit interest in geomedia technologies, yet their goals, composition, and technology review processes differ. We examined working groups from four US cities—Boston, Seattle, Syracuse, and Vallejo—to identify how group members articulate different sociotechnical imaginaries of geomedia. Through interviews with working group members and an analysis of public documents, we examine how working groups imagine the future use, and misuse, of these technologies in their communities. In turn, this project highlights how multi-stakeholder governance can shape decision-making about geomedia futures.
more »
« less
- Award ID(s):
- 1952063
- PAR ID:
- 10549824
- Publisher / Repository:
- Cogitatio Press
- Date Published:
- Journal Name:
- Media and Communication
- Volume:
- 12
- ISSN:
- 2183-2439
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Individuals today discuss information and form judgements as crowds in online communities and platforms. “Wisdom of the crowd” arguments suggest that, in theory, crowds have the capacity to bring together diverse expertise, pooling distributed knowledge and thereby solving challenging and complex problems. This paper concerns one way that crowds might fall short of this ideal. A large body of research in the social psychology of small groups concerns the shared information bias, a tendency for group members to focus on common knowledge at the expense of rarer information which only one or a few individuals might possess. We investigated whether this well-known bias for small groups also impacts larger crowds of 30 participants working on Amazon’s Mechanical Turk. We found that crowds failed to adequately pool distributed facts; that they were partially biased in how they shared facts; and that individual perception of group decisions was unstable. Nonetheless, we found that aggregating individual reports from the crowd resulted in moderate performance in solving the assigned task.more » « less
-
Reporting normative feedback to residential energy consumers has been found effective at reducing residential energy consumption. Upon receiving normative feedback households tend to modify their use to become in line with group norms. The effect of normative messages is partially moderated by how personally relevant normative reference groups are to the individual. Advanced energy metering technologies capture households’ energy use patterns, making it possible to generate highly similar and relevant normative reference groups in a non-invasive manner. Unfortunately, it is not well understood how similar individuals are to other group members. It also remains unknown how much individuals identify with behavioral reference groups. Therefore, this research aims to investigate how households perceive behavioral reference groups used in normative comparisons. Survey questionnaires are collected from 2,008 participants using Amazon Mechanical Turk. It is found that while households’ behaviors are more similar when grouped based on energy use profiles than based on geographic proximity, they identify more closely with proximity-based groups. Also, members’ group identification increases as individuals have higher similarity in energy use behaviors with other group members. This implies that enhancing the identity of profile-based behavioral reference groups will lead to an increase in norm adherence, and in turn reductions in household energy use.more » « less
-
Marshall, James AR (Ed.)Despite almost a century of research on energetics in biological systems, we still cannot explain energy regulation in social groups, like ant colonies. How do individuals regulate their collective activity without a centralized control system? What is the role of social interactions in distributing the workload amongst group members? And how does the group save energy by avoiding being constantly active? We offer new insight into these questions by studying an intuitive compartmental model, calibrated with and compared to data on ant colonies. The model describes a previously unexplored balance between positive and negative social feedback driven by individual activity: when activity levels are low, the presence of active individuals stimulates inactive individuals to start working; when activity levels are high, however, active individuals inhibit each other, effectively capping the proportion of active individuals at any one time. Through the analysis of the system’s stability, we demonstrate that this balance results in energetic spending at the group level growing proportionally slower than the group size. Our finding is reminiscent of Kleiber’s law of metabolic scaling in unitary organisms and highlights the critical role of social interactions in driving the collective energetic efficiency of group-living organisms.more » « less
-
Smart home technologies on our phones and in our homes are collecting more (quantity) and more (types of) data about us. These tools are intended to simplify everyday tasks, but to do so effectively, they collect significant data about users and their environment. One popular example includes intelligent personal assistants (IPAs) like Apple’s Siri, Amazon’s Alexa, and Google’s Assistant. As these services have transitioned from being embedded in consumers’ smartphones to standalone devices in the home, they have evolved to collect and share more data in more potentially invasive ways. In this paper, we consider American adults’ attitudes toward IPAs. Through an analysis of focus groups with IPA users and non-users, we describe attitudes and concerns toward IPAs broadly, as well as how these attitudes vary based on the devices’ features. We suggest new frameworks for evaluating privacy-based decisions and offer policy suggestions for regulating data flows from smart technologies.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.17645/mac.8201
