Bowling leagues and political participation

For some reason it’s weird to say out loud, or to type, but I was in a bowling league in high school. And, for me, I think I can comfortably say that it didn’t impact my likelihood to participate in the political system, integrate me with individuals that I could count on for support, pay off in the form of material benefits later in life, or increase my capacity to weather difficult times. In fact, I can say with complete confidence that I haven’t spoken a word to anyone remotely associated with said bowling league in ten years. Surprisingly, this didn’t discourage me from relentlessly citing Putnam as an undergraduate, because it’s kind of a wonderful ideology to make sense of both macro issues like our failed government, and micro issues, such as “why didn’t that jerk try to hold the door for me?”, and, since I don’t have a point of comparison to a time when even the cool kids were in bowling leagues, I can’t really dismiss the theory. Times have changed a lot since I was in high school though, and when I look around today I see people as politically engaged as they’ve ever been, and, through technology, people communicating more than, in all likelihood, any generation ever has. As Portes (2002) indicates, perhaps Putnam’s error is in treating an egocentric measure such as social capital in such a way that it could be measured like GDP, which certainly seems like some sort-of methodological fallacy, or maybe it’s just a good old case of rose tinted hindsight glasses, and while I’m certainly not going to disagree with the premise that high measurements of social capital are a positive indicator for any given individual, I don’t think Putnam’s writing has a lot of explanatory power relative to the political world. That said, I have always been sympathetic to his corollary point, which is that collective social capital is declining because people are watching absurd amounts of television – I doubt that collective social capital is declining significantly, but people are definitely watching an absurd amount of television, which always seemed like something that would be interesting to investigate. Also, to be fair, nobody goes bowling alone, they’re just not in silly competitive leagues anymore.

My critique of social capital is twofold: 1) it’s association with the term “capital,” which, for me, must refer to a mechanism for maintaining relative class privilege, and 2) the concept from which it’s derived, capital, is already a social innovation. That’s not to say that I’ve reversed course, and will now use my blogs as a space to trivialize the importance of social networks – there is power in social organization, boons and consequences are inherent in every web of relationships, and our degree of social integration demonstrably shields us from diminished life outcomes – but, as Kadushin (2012) argues, comparing these concepts to financial capital produces something of a tortured simile. Labor exploitation, extraction, and colonial projects yielded financial capital. Europe did not set the world on fire to make friends; instead, they proposed a radically exploitative mode of financially deterministic relations to govern the world. If Warren Buffet runs out of sugar tomorrow morning, he might try to borrow some from his neighbor and establish a lender’s relationship of reciprocity, but until the economy of that friendship becomes commensurable with the extraordinary social upheaval that he might cause by, perhaps, taking a massive short position on a major employer simply because he has a hunch, I don’t really think it’s fair to suggest that these concepts are significantly comparable.

 

in brief

In brief

Can one meaningfully quantify class? This, according to Fourcade & Healy (2013)⁠, is what has occurred in contemporary liberal states – except, instead of being used by social scientists, who would love to use these metrics “were they not trade secrets” (Fourcade & Healy, 2013, p. 570)⁠, it is, rather, employed by the financial industry in order to control access to credit, and the broader financial system (and, as is the trend within neoliberalism, this market logic is increasingly applied to non-market situations in individuals’ lives). Credit scoring, a nominally nonracial and nongendered project, evidently encapsulates more predictive information about an individual’s performance in a market economy than the whole of social scientific research, yet, as social science research indicates, traditional cleavages in regards to life outcomes still persist, which pivot on such axes of oppression as race and gender. The intersection of these seemingly contradictory conclusions is where Fourcade & Healy (2013) theorize, trying to decipher the nature of stratification in a financialized world.

The algorithms that control these metrics, it is argued, are unique in that, by design, they delineate access to material resources on an individuated basis, instead of upon the basis of such group classifications as race, class, and gender. However, these algorithms don’t produce class, but, rather, classifications, which are used as criteria by gatekeepers who govern access to resources. The less access an individual has, the more they are forced to rely upon substandard financial products such as payday loans and subprime mortgages, creating a feedback loop between the classification metric and market performance. No longer burdened by overtly discriminatory criteria, the financial industry, as well as broader society, is able to shroud this classification scheme in the language of morality, encouraging consumers to volunteer the self-surveillance of their own consumption patterns, as only those individuals with enough so-called financial literacy are deemed worthy of upward mobility. Yet, as Fourcade & Healy (2013)⁠ assert, these consumption patterns are more frequently determined by market position than moralistic thriftiness, as the poor are forced to pay higher proportions of lenders’ financing costs than their wealthy counterparts, in a sort-of self-fulfilling prophecy of financial proletariatization.

Fourcade, M., & Healy, K. (2013). Classification situations: Life-chances in the neoliberal era. Accounting, Organizations and Society, 38(8), 559–572. https://doi.org/10.1016/j.aos.2013.11.002

 

Blog 2: The Social Network Analysis Perspective

The social network analysis perspective is a mode of analysis that unites one of sociology’s fundamental objects of study, the intersection between the individual and society, with modern analysis techniques. Specifically, social network analysis is rooted in Moreno’s sociometric analysis techniques, the “Harvard structuralist” school, and the Manchester anthropologist tradition (Keim, 2011)⁠. Whereas traditional quantitative projects are suited for comparing individuals and populations, social network analysis techniques allow the researcher to study specific relationships between actors, both directly and indirectly, in order to answer research questions that consider social relationships as a primary concern, such as research into socialization phenomena.

Because social network analysts consider networks as opposed to comparing individuals or groups, the structure of the data they analyze consequently changes. Traditional research often focuses on measurements about these individuals or groups, taken in a vacuum that ignores their interactions. Conversely, social network analysts primarily study data directly relevant to the interaction of two actors. Specifically, this is known as relational data, and is typically understood as the data that specifies any two nodes’ relation to each other. This could be as simple as stating that a relationship between two actors exists, but could also specify the direction of that relationship, or other qualities such as whether or not a relationship is antagonistic.

In a sense, relational data can be seen as similar to descriptive statistics in traditional studies, but quantitative social network analyses can go a little further. Descriptive analysis techniques can also be applied to a social network representation, allowing for measurements of centrality, distance between nodes, and clustering. Predictive analysis will, of course, demand an appropriate research question, but, given an adequate representation of the social network, researchers will find that social network analysis yields significant predictive power.

Take, for example, the Richmond Police Department. Police departments across the country have employed a variety of predictive techniques in order to police more effectively, but, according to reporting by The Economist (2010), the RPD has included social network analysis within their toolkit. According to the article, Richmond police officers have to come to maintain relational data on suspects and their acquaintances. Furthermore, it is reported that they mine social media for data, especially in relation to predicting potential locations of parties. Of course, critics suggest that the end result of these techniques’ application has done nothing but reinforce problematic patterns of stereotyping and over-policing, but, according to the article, RPD and other departments employing similar predictive techniques claim to be able to reduce police labor-hours and, consequently, costs.


Keim, S. (2011). The Social Network Perspective. In Social Networks and Family Formation Processes (pp. 19–30). Hackensack NJ: VS Research.

Mining social networks: Understanding the social web. (2010, September). The Economist. Retrieved from http://www.economist.com/node/16910031

 

Six degrees of separation

In Christkis and Fowler’s (2009) work, Connected, the authors’ fundamental argument is that people’s behavior and emotions are affected by the people that they know, the people that those people know, and so on – in other words, by the social network which an individual is integrated in. The authors present four theses about life within social networks: 1) individuals shape their networks, meaning that individual preferences has some effects on their social network’s size and shape 2) that networks shape individuals, in that patterns in networks, such as the amount of connections somebody has, significantly affects that individual’s life outcomes, 3) that an individual’s friends affect them, in that individuals often emulate their friends’ behavior or emotional state, and 4) that an individual’s friends’ friends affect them (and friends’ friends’ friends, etc.), which is an extension of the third rule – an individual’s friends’ friends can affect that individual’s friends, which, in turn, can affect the hypothetical individual in a chain reaction. Actions, behaviors, and feelings of people that you might not even know can create a cascading reaction, distributing the effect throughout the social network like a contagion.

In many ways, this is common sense. As an example, the authors present one of Stanley Milgram’s experiments, in which Milgram uses a small crowd of people in New York City, who look at a specific building’s window at a specific time, in order to prime those around them to do the same thing. Milgram is interested in determining the critical point – how large of a crowd is needed to successfully prime typical New York pedestrian traffic to emulate the behavior? This is similar to the frequently repeated joke, which suggests that lines are so ubiquitous in contemporary society that people will queue up in an existing line simply because it’s there, even without knowing what the line is for, Milgram is able to successfully manipulate the individuals who weren’t “in” on the experiment to repeat the behavior.

Another example of this phenomenon: William Whyte’s (1988)⁠ report and short film The Social Life of Small Urban Spaces. Whyte is tasked with trying to figure out why some plazas in New York City are able to attract crowds, and others are not, and comes up with some common sense observations. For example, Whyte observes that people are attracted to kiosks and food stands which, in turn, draws more people into the area, creating a clustering effect. Similarly, Whyte’s primary thesis is that available seating area is key – when enough people are sitting in a plaza, it invites other individuals to share the space.

The Milgram and Whyte experiments may seem like common sense, but many of the observations that Christakis and Fowler (2009)⁠ make are not. The authors identify surprising social epidemics that can be traced through social networks such as smoking, depression, voting, and even suicide. As the authors assert, given that social networks are increasingly identified as a causal factor in the spread of various social phenomena, understanding the form of these social networks is increasingly important. To this end, diagramming these networks with nodes that represent individuals, documenting their interpersonal relationships, as well as the shape and size of these networks in their totality can serve as a powerful analytical tool.


Works Cited

Christakis, N. A., & Fowler, J. H. (2009). Connected:How your Friends’ Friends’ Friends Affect Everything You Feel, Think, and Do. New York: Little, Brown and Company.

Whyte, W. H. (1988). The Social Life of Small Urban Spaces. USA.

 
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