Digital media targeting young children (0-5) is increasingly subject to the sociotechnical process of “platformization” (Nieborg & Poell, 2018). Research on this subject has been primarily focused on the role YouTube Kids’ recommendation systems play in the production of video content – often of dubious algorithmic provenance (Bridle, 2017; Burroughs, 2017). Yet there is scant research on the role similar algorithmic systems play in the production of the ever-growing digital market of apps for children. As such, this paper studies how apps for young children are affected by “platformization” and offers a critical analysis of the emerging “algorithmic cultures” (Striphas, 2015) of apps for children.
To understand the relationship between distribution and the production of children’s apps, this paper focuses its attention on a particular app genre that education researchers have critiqued as an unruly “Wild West”: early literacy apps (Guernsey et al., 2012) . From a software studies perspective, I critically scrutinise the platform’s distribution conditions and the “ranking cultures” (Rieder et al., 2018) influencing young children’s educational apps through the empirical analysis of 343 scraped app store search results.
By arguing how the “Wild West” of educational apps is a manifestation of algorithmic cultures, this paper problematizes the role that recommendation systems play in the distribution, access, and production of children’s apps. The discussion reveals several characteristics of the algorithmic cultures of apps for young children, including the perceptible bloating of the genre by generic free-for-download and formulaic app families. Additionally, considering the cultural logic behind this group of apps for children highlights tensions of this double-step mediation process on app stores. First, the centralized role of recommendations as gatekeepers of content for children as a vulnerable population. Second, the challenges platforms pose to digital parenting (Mascheroni et al., 2018) by operationalizing their economic priorities through algorithms.