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Affective Networks in Ensemble Character Dramas

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Saved by Alston
on May 3, 2012 at 8:34:39 am
 

YouTube user tarnationsauce2 assembled a supercut that accrues the many instances that Lt. Worf's contributions are sidelined, belittled or shut down in his interactions both professional and personal over his multi-season television show Star Trek: The Next Generation. For the viewers that might follow the show, the supercut emphasizes how frequently Worf's concerns generally turn out to be justified. Perhaps unsurprisingly, commentators underscore the racial undertones of the tendency, complicating the liberal politic of race in the Star Trek ethos.   

 

 

Through a type of deformance, this supercut seems to makes legible, tracable and definite in the aggregate affective charges of social networks what might only be a suspicion in the disaggregate, linear and discrete approach to the same material. Large ensemble cast in multi-season shows have become an increasingly common form in television. The escalation of scale in terms length, characters, plot and relations as well as the quantity of knowledge created seem to invite approaches of criticism that can exploit and come to terms with network, both within and outside the text. Our project will explore these possibilities to examine difference (race, sexuality) in texts such as Friday Night Lights and Lost by:

  • a process of deformance where we will take an episode and erase every instance of a character. By doing so, we might reflect how the text remains legible by its exclusion or whether new insights become legible.
  • networking the affective relations of characters while indicating aspects of identity made legible by the text (race, gender, national origin, access). We will draw on information from fan wikis to construct a network that spans multiple seasons, and also draw from the text itself to do a few mappings of individual episodes for comparison. Such a mapping might indicate the horizon of social relations imaginable for characters within the parameters of their universe.
  • deforming the graphed networks much in the way that we will deform the text itself, exploring how the shapes and connections in the graph change when a certain character is removed. We will also play with different graphic models for social networks, seeing what structure best represents the relationships for a given series. Along the way we will be using graph theory to analyze the networks. (For example, we might explore questions such as average "distance"--number of edges--between two nodes representing characters of the same race versus between two nodes representing characters of different races.)

If our mapping and network prove reproducible, we might also pursue networks outside the text, such as relationships of show creators across franchises. 

 

Possible Tools (or Toys)

 

Friday Night Lights

I (Hannah) have chosen to focus on Friday Night Lights, which aired on NBC from 2006-2011. This show takes place in a small town in Texas, and revolves around the town's obsession: high school football. The team becomes an interesting site for the exploration of racial tensions that run rampant in this town, but even as the show directly addresses these issues, most of its main characters are white, with only one main black character in any season. I am interested to see what happens to the logic of the show when this character is removed, and intrigued to play with the social networks with and without this character present as a node. I suspect that the overtly racial drama of the first season, in which a white coach's racist comments incite racial conflict throughout the high school, would make little sense without this character's negotiations between the black world of the background characters and the white world of the main characters--but I hope to understand this role more fully through the deformations and social network graphings we propose.

 

From a fan site, an example of mapping in another large character ensemble. Click to Enlarge. 

X-Men Universe Relationship Map 

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