To continue on the theme of last class, I think there’s an underlying connection between the mp3 and so-called Big Data, and the technological tracks they occupy. The mp3 and similar digital container technologies have been rendered largely obsolete, at least in the sense of ownership, by streaming; Big Data techniques have ushered in a clustering paradigm, both as the sought-after piece of analytical knowledge and as the natural application of that analysis, a consequence of the ubiquity of data.

How do we interact with streaming media? I think an instructive archetype is the Spotify playlist. No one’s playlist has songs that someone else couldn’t put in their own playlist, so the inherent meaning or value derives from the space/genre/cluster defined by the playlist’s songs. In this zero-sum game, now that the ownership of music means little, the taste or preference or subculture-membership of the playlist creator takes on meaning (this meaning has always been present to some degree, but I think it can be argued that the meaning implied by music object ownership has traditionally suppressed it).

How does Big Data analysis proceed? Broadly, by identifying meaning that isn’t explained by any single observed variable.

In both technological tracks we could pick out a similar shift—when our pool of data (media content, data in the scientific sense) reaches such a level of omnipresence that everyone pretty much has access to the same set, we try to determine the latent space that remains now that the noise of our informational infrastructure has become peripheral and no longer creates space of its own (although this space will surely have lasting implications), and then we try to settle into it.

It actually seems like the Editor-in-Chief of Wired had a point in emphasizing that “we can track and measure [what people do] with unprecedented fidelity,” and that to be able to do this endangers the utility of fields that cluster people by hand, as it were. To be sure, the numbers don’t speak for themselves, but for the most part they do display themselves silently in full public view.

This also seems like an unstated reason for boyd and Crawford to adopt the idea, from boyd’s previous work with Alice Marwick, that “being in public” is distinct from “being public.” How do we describe spaces that are both public and private? We could imagine a vast, unfenced, latent field (for boyd and Crawford, a park) on which groups of people cluster together for social interaction. Anyone could visit any cluster, but distance between clusters makes such travel difficult. Privacy scales with distance (which we could say scales inversely with the number of public data points about a cluster), so it’s no longer a binary concept. This is why Zack’s point last class is crucial—he said we’ve reached a point where our data is basically fully “out there,” and the only thing mediating a dox is effort.