Persistent racial inequality in employment, housing, and a wide range of other social domains has renewed interest in the possible role of discrimination. And yet, unlike in the pre-civil rights era, when racial prejudice and discrimination were overt and widespread, today discrimination is less readily identifiable, posing problems for social scientific conceptualization and measurement. This article reviews the relevant literature on discrimination, with an emphasis on racial discrimination in employment, housing, credit markets, and consumer interactions. We begin by defining discrimination and discussing relevant methods of measurement. We then provide an overview of major findings from studies of discrimination in each of the four domains; and, finally, we turn to a discussion of the individual, organizational, and structural mechanisms that may underlie contemporary forms of discrimination. This discussion seeks to orient readers to some of the key debates in the study of discrimination and to provide a roadmap for those interested in building upon this long and important line of research. Keywordsrace; inequality; measurement; mechanisms; African Americans; racial minorities Persistent racial inequality in employment, housing, and other social domains has renewed interest in the possible role of discrimination. Contemporary forms of discrimination, however, are often subtle and covert, posing problems for social scientific conceptualization and measurement. This article reviews the relevant literature on racial discrimination, providing a roadmap for scholars who wish to build on this rich and important tradition. The charge for this article was a focus on racial discrimination in employment, housing, credit markets, and consumer interactions, but many of the arguments reviewed here may also extend to other domains (e.g., education, health care, the criminal justice system) and to other types of discrimination (e.g., gender, age, sexual orientation). We begin this discussion by defining discrimination and discussing methods for measuring discrimination. We then provide an overview of major findings from studies of discrimination in employment, housing, and credit and consumer markets. Finally, we turn to a discussion of the individual, organizational, and structural mechanisms that may underlie contemporary forms of discrimination.
.The authors wish to note the following: "We reported an estimated 30% decrease in administrative reports of conflict and would like to correct this estimate to 25%. The original paper reported this estimate as the rounded covariate-adjusted estimated average treatment effect (−0.06) divided by the rounded unadjusted control group mean (0.20). Without the rounding error, the original estimate is 29%. Using both covariate adjusted estimates, we have an estimate of 25%. Using both unadjusted estimates, we have an estimate of 23%. The original estimate of 30% was reported in the Abstract (line 11), the Significance Statement (line 8), in the results on page 569 (left column, first paragraph, line 9), and in the discussion on page 571 (left column, first paragraph, line 6). In each of these places, we would like to replace the number 30 with 25. The estimated overall effects on administrative reports of conflict remain statistically insignificant at the α=0.05 level."Published under the PNAS license.
The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from tagged sites on the social bookmarking site del.icio.us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used analyze the meaning of particular tags given their relationship to other tags.
Persistent, widespread harassment in schools can be understood as a product of collective school norms that deem harassment, and behavior allowing harassment to escalate, as typical and even desirable. Thus, one approach to reducing harassment is to change students' perceptions of these collective norms. Theory suggests that the public behavior of highly connected and chronically salient actors in a group, called social referents, may provide influential cues for individuals' perception of collective norms. Using repeated, complete social network surveys of a public high school, we demonstrate that changing the public behavior of a randomly assigned subset of student social referents changes their peers' perceptions of school collective norms and their harassment behavior. Social referents exert their influence over peers' perceptions of collective norms through the mechanism of everyday social interaction, particularly interaction that is frequent and personally motivated, in contrast to interaction shaped by institutional channels like shared classes. These findings clarify the development of collective social norms: They depend on certain patterns of and motivations for social interactions within groups across time, and are not static but constantly reshaped and reproduced through these interactions. Understanding this process creates opportunities for changing collective norms and behavior.
This article uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users.First, we study the formation of stable distributions in tagging systems, seen as an implicit form of "consensus" reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail.Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine.14:2 • V. Robu et al.Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging. ACM Reference Format:Robu, V., Halpin, H., and Shepherd, H. 2009. Emergence of consensus and shared vocabularies in collaborative tagging systems.
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