Network Discrimination

Examples of Network Discrimination in the following topics:

• Women in the Workplace

  • While occupational sexism and the glass ceiling will be explored in the section 'Inequalities of work," what follows is a discussion of barriers to equal participation in the work force, including access to education and training, access to capital, network discrimination and other factors.
  • As a result, recruiters for high-status jobs are predominantly white males, and tend to hire similar people in their networks.
  • Their networks are made up of mostly white males from the same socio-economic status, which helps perpetuate their over-representation in the best jobs.

• Discrimination Against Individuals

  • Controversial attempts have been made to redress negative effects of discrimination.
  • Unfair discrimination usually follows the gender stereotypes held by a society.
  • Reverse discrimination is a term referring to discrimination against members of a dominant or majority group, including the city or state, or in favor of members of a minority or historically disadvantaged group.
  • Reverse discrimination may also be used to highlight the discrimination inherent in affirmative action programs.
  • Give an example of discrimination and reverse discrimination using examples of religious, gender, or racial prejudice

• Sexism

  • Sexism is discrimination against people based on their perceived sex or gender.
  • Despite the increase in participation in sports, major network news coverage of women's sports has changed very little over the last 15 years.
  • Another example of gender discrimination is the disparity in wealth between men and women.
  • Women in some organizations are suing their employers claiming gender discrimination.
  • It is difficult to prove discrimination in such cases.

• Institutional Prejudice or Discrimination

  • Institutionalized discrimination refers to discrimination embedded in the procedures, policies or objectives of large organizations.
  • Institutionalized discrimination within the housing market also includes practices like redlining and mortgage discrimination.
  • Institutionalized discrimination within the housing market also includes practices like redlining and mortgage discrimination.
  • The achievement gap in education is another example of institutionalized discrimination.
  • Examine the legal cases that had an impact on institutional discrimination

• Categorical REGE for geodesic distances (Padgett's marriage data)

  • The categorical REGE algorithm (Network>Roles & Positions>Maximal Regular>CATREGE) can be used to identify regularly equivalent actors by treating the elements of the geodesic distance matrix as describing "types" of ties -- that is different geodesic distances are treated as "qualitatively" rather than "quantitatively" different.
  • Since the data are highly connected and geodesic distances are short, we are not able to discriminate highly distinctive regular classes in these data.

• Scales of measurement

  • We will briefly describe all of these variations, and provide examples of how they are commonly applied in social network studies.
  • Network analysts are often concerned with describing the "strength" of ties.
  • The most commonly used algorithms for the analysis of social networks have been designed for binary data.
  • The most powerful insights of network analysis, and many of the mathematical and graphical tools used by network analysts were developed for simple graphs (i.e. binary, undirected).
  • Many characterizations of the embeddedness of actors in their networks, and of the networks themselves are most commonly thought of in discrete terms in the research literature.

• Social Networks

  • Facebook is an example of a large social network.
  • Social networks are composed of nodes and ties.
  • Smaller, tighter networks composed of strong ties behave differently than larger, looser networks of weak ties.
  • The study of social networks is called either social network analysis or social network theory.
  • Assess the role of social networks in the socialization of people

• Modality and levels of analysis

  • The network analyst tends to see individual people nested within networks of face-to-face relations with other persons.
  • Often these networks of interpersonal relations become "social facts" and take on a life of their own.
  • A family, for example, is a network of close relations among a set of people.
  • Most social network analysts think of individual persons as being embedded in networks that are embedded in networks that are embedded in networks.
  • In chapter 17, we'll take a look at some methods for multi-mode networks.

• Summary

  • In this chapter we've taken a look at some of the most basic and common approaches to applying statistical analysis to the attributes of actors embedded in networks, the relations among these actors, and the similarities between multiple relational networks connecting the same actors.
  • But, there is still a good bit more, as the application of statistical modeling to network data is one of the "leading edges" of the field of social (and other) network analyses.
  • First, for very large networks, methods for finding and describing the distributions of network features provide important tools for understanding the likely patterns of behavior of the whole network and the actors embedded in it.
  • Second, we have increasingly come to realize that the relations we see among actors in a network at a point in time are best seen as probabilistic ("stochastic") outcomes of underlying processes of evolution of networks, and probabilistic actions of actors embedded in those networks.
  • And, we've taken a look at a variety of approaches that relate attributes of actors to their positions in networks.

• Introduction

  • The basic idea of a social network is very simple.
  • Networks can have few or many actors, and one or more kinds of relations between pairs of actors.
  • The amount of information that we need to describe even small social networks can be quite great.
  • All of the tasks of social network methods are made easier by using tools from mathematics.
  • For the manipulation of network data, and the calculation of indexes describing networks, it is most useful to record information as matrices.