Netflix Nations. Ramon Lobato

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Netflix Nations - Ramon  Lobato Critical Cultural Communication

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economies, and identities. Of course, Netflix is not a platform in the same way as social media services like Facebook or Twitter are. Netflix is not open, social, or collaborative. One cannot upload content to Netflix or design software applications to run within it. In this sense, it is fundamentally different from video sites containing both user-uploaded and professionally managed content (YouTube, Youku, etc.). Unlike these sites, Netflix does not (at this stage) have an advertising business model; nor does it have the character of a multisided marketplace like Amazon or Ebay, which host a more complicated ecology of commercial activity. Netflix is closed, library-like, professional; a portal rather than a platform; a walled garden rather than an open marketplace. This said, we can still learn a lot about Netflix through platform studies perspectives.

      Platform studies has evolved along two main lines. The first of these comes out of the work of Nick Montfort and Ian Bogost. In their book Racing the Beam: The Atari Video Computer System and related working papers—which are widely read in games studies, though less so in television studies—Montfort and Bogost outline a specific understanding of platforms and how they can be studied. They define a platform as the “hardware and software framework that supports other programs” (Bogost and Montfort 2009a, 1) and as “a computing system of any sort upon which further computing development can be done” (Bogost and Montfort 2009b, 2). They note that a “platform in its purest form is an abstraction, simply a standard or specification” (Bogost and Montfort 2009a, 1). Consequently, their vision of platform studies involves “investigating the relationships between the hardware and software design of standardized computing systems and the creative works produced on those platforms” (ibid.). Montfort and Bogost insist that researchers pay close attention to the materiality of the platform, including its design, construction, and even wiring, as well as to the platform’s user-facing and symbolic dimensions. Their approach is better suited to gaming systems such as Atari and PlayStation than to online services like YouTube, Steam, or Netflix—though the material dimensions of the latter are also amenable to research and critique, as we will see in Chapter 3.

      A second strand of thinking about platforms comes out of critical communications and internet research. The work of Tarleton Gillespie in particular draws our attention to the expanding range of everyday communication and consumption practices that take place within online platforms, especially social media networks. Gillespie defines platforms as “sites and services that host, organize, and circulate users’ shared content” (Gillespie 2017, 254). His essay “The Politics of ‘Platforms’ ” (Gillespie 2010) was an early critique of the way online services such as Facebook and YouTube strategically defined themselves as neutral intermediaries—as technology companies rather than media companies—thus obscuring their power as mediators of communication, identity, and politics.2

      A key theme in Gillespie’s work is the agency of the platform itself. Far from being neutral, platforms shape the communications, interactions, and consumption that they facilitate—through interface design, moderation policies, terms of service, algorithmic recommendation, and so on. Consider the Facebook “Like” button and how it subtly institutes a norm of extroverted positivity as the default practice for online communications—there is no “Dislike” or “Don’t Care” button—while at the same time generating valuable commercial data for Facebook by turning “personal data into … public connections” (van Dijck 2013, 49; Gerlitz and Helmond 2013). We should not, then, make the mistake of seeing a platform as a “neutral” distributor of content, because the nature, design, and business model of the platform will always have an effect on what passes through it. Platforms, according to Gillespie,

      have precise (and shifting) technical affordances that constrain and guide practice—both in their own design and in their fit with a myriad of infrastructures, including their back-end data systems, the protocols of the Web, and the dictates of mobile providers. They have rules and norms that bless some practices and are used to restrict others. They have myriad international, sometimes conflicting, legal obligations they must enforce. They have commercial aspirations and pressures that drive decisions about how they’re marketed, how they’re updated, and how they’re positioned against their competitors. (Gillespie in Clark et al. 2014, 1447)

      Following Gillespie’s arguments, it is possible to see how Netflix—while certainly not a social media platform—exploits the same quality of discursive slipperiness as these other platforms. Netflix, like Facebook and YouTube, is presently engaged in a number of disputes with government agencies about how and whether it should censor its film and television content. In India, for example, Netflix claims that because it is an internet-delivered service rather than a broadcaster, it should not have to follow the obscenity policies that apply to Indian television stations (see Chapter 4). This is not all that far from Uber’s insistence that because it is a technology platform it should not have to follow the licensing and tax laws that apply to taxi companies, or Facebook’s insistence that it is not a media company and therefore should not have to fully regulate the communications taking place through its networks. In each case, a service’s digital status is invoked to sidestep regulatory responsibilities.

      Even though these three companies operate in very different markets (transport, communications/advertising, and scripted entertainment), they have a common operational logic that hinges on their status as a digital service that is (a) categorically dissimilar to the established incumbents they now compete with and (b) operating in global markets from a U.S. base, partially outside the jurisdictional reach of national governments. Following this logic, and notwithstanding the lines of historical evolution between Netflix and television traced in the previous section, one can also argue that these structural similarities with other digital services place Netflix within the platform economy as much as within the entertainment industries.

      Another common characteristic of digital media platforms is a reliance on algorithmic recommendations. Along with Amazon and Pandora, Netflix has played a pivotal role in the development and popularization of recommendations generally, having invested heavily in this area since its years as a DVD rental service. The company famously ran an open engineering competition, the US$1 million Netflix Prize of 2006–2009, to improve its predictive powers by 10%. The fruits of these efforts have paid off in the form of its eerily accurate prediction engine, which seeks to, in Hastings’s words, “get so good at suggestions that we’re able to show you exactly the right film or TV show for your mood when you turn on Netflix” (The Economist 2017). On the Netflix home screen, algorithmic recommendations are used to autocurate selections of content geared around individual users’ data profiles. Every video selection that appears on the home screen is the result of intricate calculations based on user-submitted data (movie ratings and viewing history), collaborative filtering (predictions based on other people’s activities), and manual coding of films for all conceivable metadata points, from character types to endings.

      This naturally puts Netflix squarely in the middle of debates about the datafication of culture, filter bubbles, and big-data politics (Pariser 2011; boyd and Crawford 2012; Beer 2013). Its recommendation system has been accused of everything from unjustified consumer surveillance to the demise of the mass audience and the end of serendipity. Film scholars in particular have voiced concern about the way personalization leads to filter bubbles. In an essay on Netflix’s “mathematization of taste,” Neta Alexander (2016, 94) warns that “the rise of predictive personalization might be good news for the study of artificial intelligence and machine learning, but it is bad news for anyone who wishes to encounter what Sontag calls ‘great films.’ ” We should, however, bear in mind that algorithms can be programmed for diversity as well as for taste reproduction (Blakley 2016).

      The debate about Netflix’s effect on taste and consumption continues to rage, though it is not a primary focus of this book. For our purposes, let us instead focus on the design of the Netflix interface and how this mediates relations between television, cinema, and digital media. The Netflix interface changes regularly but at the time of writing is organized into categories that are curated automatically

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