Experiences and fun facts from videogame practice & research
18 October 2020
Dustin Kidd is a professor of sociology at Temple University of Philadelphia. In his book, using a metaphor of freaks being everyone and anyone not just people ostracized and pushed to the rims of society, he aims to explore how popular culture creates a sense of closeness and remoteness for everyone. The theoretical concept he uses in his book draw from sociology (e.g. reflection theory, feminist theories), economy, communication studies, cultural studies, and reception studies. Mostly, he focuses on the intersection of identity in popular culture. Kidd aims to answer a research question on how identity influences popular culture through five dimensions of identity including race, class, gender, sexuality, and disability, adding a global perspective as a summarizing factor to his analysis.
Kidd answers his question by a combination of three main arguments. First, he argues that the goals celebrated by commercial culture are unobtainable through the means presented by that culture, tossing the consumers into a perpetual struggle to achieve something they are told to achieve but that will never be able to achieve. Kidd points out that this is what a sociologist Robert Merton calls being a ritualist. Even though popular culture normalizes our identities, it also brands us as freaks and strangers. We might want to achieve all the cultural goals presented to us in popular culture, but the means to achieve those goals never actually get us there. His second argument states that to understand popular culture, as researchers we need to pay attention to the relationships between creators (producers) and receivers (consumers) and social worlds (the community in which the cultural object acts) and cultural objects themselves. This is also known as the cultural diamond developed by Wendy Griswold in the 90s. Kidd is especially interested in how producers who misrepresent the lives of groups they are not familiar with or have a skewed idea of might present them in popular culture, further reinforcing stereotypes and images recurring about the group in the media. In his third argument directly connected to the previous one, identity influences popular culture by creating deep disparities, especially found in the labor force demographic for production or quantitative and qualitative representations in the content. Kidd places importance on both qualitative and quantitative aspects of research, promoting identity as a mechanism of stratification and privilege when stereotypes and other kinds of images that produce distorted notions about certain groups are overlooked or even reinforced.
Additionally to the introductory chapter, I also read Chapter 7 about global identity expressed through the phenomenon of Harry Potter. Here Kidd, by using a variety of examples he calls “postcards’’ from countries such as India, Korea, etc., shows how identity and popular culture cannot be examined just locally but should be researched through globalized lenses. As mentioned, he uses Harry Potter and how it has been translated, marketed, and perceived throughout the world, to make his case. He points out that identity-based inequalities are universal, their dimensions and character do vary from one cultural setting to another. Kidd shows on examples that as much as differences are important (and suitable for his “freak” framing of the book), common human experiences of identity can help understand those differences better. He draws on the sociological concept of symbolic interactionism, which examines the way meaning shapes behavior of individuals toward the objects and people in the world around them, and significant symbols, which are layered in shared meaning. Kidd argues that popular culture is one of the greatest producers of shared meanings aka significant symbols, however the risk of misinterpretation or missing something in the translation increase as culture moves across geographical and political boundaries (e.g. translating Harry Potter books even from English to American English because of mythological differences, and process of McDonaldization).
Kidd’s book is an invaluable toolkit for me as a novice of reception theory, audience ethnography, and other methods he uses and explains throughout the book. Even though I haven’t read the whole book, I paid attention to the parts where he details what each of the methods he used is about and what it aims to accomplish. This will be a good resource for me to build up a methodology of a future research project combining this field and algorithm studies.
Circling back to the algorithm studies, I found the part mentioning symbolic interactionism and significant symbols as fruitful paths to follow and potentially connect it to the algorithm studies, specifically to Gillespie’s concept of production of calculated publics. Netflix (and other streaming services) produce content that is being deployed globally. There are always some sort of limitations (legal, language-based, commercial, etc.) but most of the countries Netflix operates in eventually get the full scope of Netflix’s titles. When these titles reach their targeted audiences, calculated based on the data users leave behind on the platform by interacting with its content, they do so by finding patterns of similarities and differences in the data. The content itself contains shared meaning on different levels. When something is shared across the countries, the platform a) aggregates the data of users who might share these meanings, b) produces content that has these shared meanings, c) influences its users to reinforce the shared meanings they see…. I haven’t fully grasped the gist of my own thoughts, mostly because I’m lacking the vocabulary of semiotics, but I sense that shared meaning and algorithms is a way to go. Don’t know where, though…..yet.
On the more granular level (of developing algorithms), I am wondering how machines imitate capturing meaning in the data they work with to produce recommendations. I wonder if the team that stands behind the decisions written into the code of algorithms used on a platform like Netflix, take in consideration the dimensions of meaning, meaning coding and meaning decoding. Since meaning is a tacit knowledge that is learned and cultivated throughout our lives and exposure to other cultures, customs and habits, is there a way how to teach this (dynamically and continuously) a machine? How would we code it? How would we update it?
Kidd, D. (2018). Pop culture freaks: Identity, mass media, and society. Routledge.
Featured picture is from http://www.dustinkidd.net/pop-culture-freaks-2nd-edition