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Podcast, Nick Seaver: “What Do People Do All Day?” - MIT Comparative Media Studies/Writing
"The algorithmic infrastructures of the internet are made by a weird cast of characters: rock stars, gurus, ninjas, wizards, alchemists, park rangers, gardeners, plumbers, and janitors can all be found sitting at computers in otherwise unremarkable offices, typing. These job titles, sometimes official, sometimes informal, are a striking feature of internet industries. They mark jobs as novel or hip, contrasting starkly with the sedentary screenwork of programming. But is that all they do? In this talk, drawing on several years of fieldwork with the developers of algorithmic music recommenders, Seaver describes how these terms help people make sense of new kinds of jobs and their positions within new infrastructures. They draw analogies that fit into existing prestige hierarchies (rockstars and janitors) or relationships to craft and technique (gardeners and alchemists). They aspire to particular imaginations of mastery (gurus and ninjas). Critics of big data have drawn attention to the importance of metaphors in framing public and commercial understandings of data, its biases and origins. The metaphorical borrowings of role terms serve a similar function, highlighting some features at the expense of others and shaping emerging professions in their image. If we want to make sense of new algorithmic industries, we’ll need to understand how they make sense of themselves.

Nick Seaver is assistant professor of anthropology at Tufts University. His current research examines the cultural life of algorithms for understanding and recommending music. He received a masters from CMS in 2010 for research on the history of the player piano."

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nickseaver  2016  work  labor  algorithms  bigdata  music  productivity  automation  care  maintenance  programming  computing  hierarchy  economics  data  datascience 
february 2018 by robertogreco
Critical Algorithm Studies: a Reading List | Social Media Collective
"This list is an attempt to collect and categorize a growing critical literature on algorithms as social concerns. The work included spans sociology, anthropology, science and technology studies, geography, communication, media studies, and legal studies, among others. Our interest in assembling this list was to catalog the emergence of “algorithms” as objects of interest for disciplines beyond mathematics, computer science, and software engineering.

As a result, our list does not contain much writing by computer scientists, nor does it cover potentially relevant work on topics such as quantification, rationalization, automation, software more generally, or big data, although these interests are well-represented in these works’ reference sections of the essays themselves.

This area is growing in size and popularity so quickly that many contributions are popping up without reference to work from disciplinary neighbors. One goal for this list is to help nascent scholars of algorithms to identify broader conversations across disciplines and to avoid reinventing the wheel or falling into analytic traps that other scholars have already identified. We also thought it would be useful, especially for those teaching these materials, to try to loosely categorize it. The organization of the list is meant merely as a first-pass, provisional sense-making effort. Within categories the entries are offered in chronological order, to help make sense of these rapid developments.

In light of all of those limitations, we encourage you to see it as an unfinished document, and we welcome comments. These could be recommendations of other work to include, suggestions on how to reclassify a particular entry, or ideas for reorganizing the categories themselves. Please use the comment space at the bottom of the page to offer suggestions and criticism; we will try to update the list in light of these suggestions.

Tarleton Gillespie and Nick Seaver"
algorithms  bibliography  ethics  bigdata  tarletongillespie  nickseaver  2016  sociology  anthropology  science  technology  criticalalgorithmstudies  via:tealtan 
june 2016 by robertogreco
Danielle Carr on Twitter: "So many critiques of quantification rely on the premise of an untrammeled wholeness that is sullied by description through numbers."
"So many critiques of quantification rely on the premise of an untrammeled wholeness that is sullied by description through numbers." [*two replies below)

"This idea of language as that which severs us from reality is precisely the lacanian critique of language as a traumatic alienation."

"So if we think of quantification as a form of nomination (if not of language as such), we miss something by insisting on its lack"

"So much of "qualitative" social methods justifies itself methodologically by decrying the lack instantiated by quantification"

"But languages aren't lack. They are the introduction of new associative capacities. We must think of any nominative system as PRODUCTIVE"

"The question then, of course, is what is produced."

"It's easy to resent the hegemonic episteme of DATA, and yes, quantification is making absurd claims (eg literary analysis by word frequency)"

"But nominative schemes introduce possibilities for linking things, often through equating one thing with another"

"One thing is equivalent to another within the nominative scheme- my depression is equivalent to yours because we scored the same on a metric"

"Does this linkage erase the "realness"reality? Of the deep social contextuality of our respective depression, etc?"

"Only if we take the assertion of identity seriously rather than as an associative capacity emerging from a play of language"

"My point is this: don't critique quantification like a lacanian dickhead or you'll miss the fun of nominative play"

"One thing you'll learn from doing STS ethnography real quick: NOBODY THINKS THE NUMBERS ARE AN EXHAUSTIVE DESCRIPTION OF REALITY"

"so sociological critiques that mouth "quantification is arbitrary/inadequate" are reaaaaaally old news to the actants in question"

"Saying "numbers aren't adequate descriptions of reality" is fatuous because THERE IS NO ADEQUATE DESCRIPTION OF REALITY"

"Description is itself a productive capacity of reality. So we have to ask what the descriptions do."

"Anyway I love sociological critique, I really do, but can we please stop pretending that language is real and numbers are arbitrary"


"@flaneuryoconnor Yeah, this is dear to me—I wrote about it in this piece for Prickly Paradigm, "Bastard Algebra": …"

"@flaneuryoconnor Yeah, sucks. Have you seen this special issue? A mixed bag, but these folks are working beyond that …"]
daniellecarr  nickseaver  data  quantification  2016  reality  words  language  lacan  traumaticalienation  realness  context  sts  ethnography  numbers  sociology  description  perception  arbitrariness 
february 2016 by robertogreco
The Internet is Like Water — Global, with Extreme Differences in Access — Medium
"Turn on a faucet in Manhattan, and you can be pretty certain the water you get out of the tap is potable. Though famously a dirty city, New York City provides some of the cleanest water in the United States, easy and at ready for those who live there. Internet access in much of the city is a little like that, too. Turn on your phone or tap into a wireless network, and data flow through seamlessly, thanks to powerful infrastructure that’s largely invisible to the average user.

The experience of the internet in a developing country can be quite different. Take, for instance, Manila, the capital of the Philippines. Many people access the internet via pocket wifi routers, which can be more readily available than physical landline connections. These routers work semi-reliably — in spurts, rather than a constant stream — , and people often carry multiple routers and network subscriptions to optimize for the best flow. On occasion, and sometimes far too frequently, the routers don’t work at all.

A young woman in Uganda gathers water from a well to carry back to her family. Photo by the author.

In other places, like Beijing, the internet and its infrastructure can be fairly reliable. But what comes through the pipes may need to be questioned and filtered. Many citizens of means curse the Great Firewall for slowing down or blocking entirely their access to the web, and they find other ways to go online, thanks to VPNs, proxies and other services. Most, however, live with an internet that is censored by both algorithmic and human means; depending on their priorities, this may or may not matter. But the fact remains that choice of what to access is limited.

And in a more rural area like the Oyam District of northern Uganda, internet access can often look more like a well. As I shared in a recent essay for The New Inquiry and a talk at the Theorizing the Web conference this year, “sneakernets” of data crop up in unexpected places. In very rural, no-bandwidth contexts, people find ways to trade data thanks to Bluetooth transfers, USB sticks, SD cards and other methods. The access point to the formal backbone of the internet might be hundreds of miles away, and in this regard, data is transferred hand to hand, rather than node to node. (This is literally how many rural Ugandans access their water, too.)

In other words, data flow, data spurt and data can be gathered. This metaphor matters because, just like with water access, the way people access the internet is highly stratified. Understanding this should inform how we think about policies and development strategies, especially as the web extends into the global south.

1. Shifting from a connectivity binary to a spectrum gives us a much richer view of the diversity of ways people access the internet.

The connectivity binary is the view that there is a single mode of connecting to the internet — one person, one device, one always-on subscription— rather than a spectrum.
The connectivity binary makes other modes of access invisible.

2. The internet probably has a larger impact than is currently measured, and we need better maps that reflect this.

3. In the face of scarcity, early internet access is often motivated by joy, social connection and entertainment — more so than education or politics per se.

2015  anxiaomina  internet  infrastructure  water  nickseaver  sneakernet  access  uganda  beijing  china  philippines  nyc  us 
november 2015 by robertogreco
On Reverse Engineering — Anthropology and Algorithms — Medium
"As a cultural anthropologist in the middle of a long-term research project on algorithmic filtering systems, I am very interested in how people think about companies like Netflix, which take engineering practices and apply them to cultural materials. In the popular imagination, these do not go well together: engineering is about universalizable things like effectiveness, rationality, and algorithms, while culture is about subjective and particular things, like taste, creativity, and artistic expression. Technology and culture, we suppose, make an uneasy mix. When Felix Salmon, in his response to Madrigal’s feature, complains about “the systematization of the ineffable,” he is drawing on this common sense: engineers who try to wrangle with culture inevitably botch it up.

Yet, in spite of their reputations, we always seem to find technology and culture intertwined. The culturally-oriented engineering of companies like Netflix is a quite explicit case, but there are many others. Movies, for example, are a cultural form dependent on a complicated system of technical devices — cameras, editing equipment, distribution systems, and so on. Technologies that seem strictly practical — like the Māori eel trap pictured above—are influenced by ideas about effectiveness, desired outcomes, and interpretations of the natural world, all of which vary cross-culturally. We may talk about technology and culture as though they were independent domains, but in practice, they never stay where they belong. Technology’s straightforwardness and culture’s contingency bleed into each other.

This can make it hard to talk about what happens when engineers take on cultural objects. We might suppose that it is a kind of invasion: The rationalizers and quantifiers are over the ridge! They’re coming for our sensitive expressions of the human condition! But if technology and culture are already mixed up with each other, then this doesn’t make much sense. Aren’t the rationalizers expressing their own cultural ideas? Aren’t our sensitive expressions dependent on our tools? In the present moment, as companies like Netflix proliferate, stories trying to make sense of the relationship between culture and technology also proliferate. In my own research, I examine these stories, as told by people from a variety of positions relative to the technology in question. There are many such stories, and they can have far-reaching consequences for how technical systems are designed, built, evaluated, and understood."

"So what does “reverse engineering” mean? What kind of things can be reverse engineered? What assumptions does reverse engineering make about its objects? Like any frame, reverse engineering constrains as well as enables the presentation of certain stories. I want to suggest here that, while reverse engineering might be a useful strategy for figuring out how an existing technology works, it is less useful for telling us how it came to work that way. Because reverse engineering starts from a finished technical object, it misses the accidents that happened along the way — the abandoned paths, the unusual stories behind features that made it to release, moments of interpretation, arbitrary choice, and failure. Decisions that seemed rather uncertain and subjective as they were being made come to appear necessary in retrospect. Engineering looks a lot different in reverse."

"All engineering mixes culture and technology. Even Madrigal’s “reverse engineering” does not stay put in technical bounds: he supplements the work of his bot by talking with people, drawing on their interpretations and offering his own, reading the altgenres, populated with serendipitous algorithmic accidents, as “a window unto the American soul.” Engineers, reverse and otherwise, have cultural lives, and these lives inform their technical work. To see these effects, we need to get beyond the idea that the technical and the cultural are necessarily distinct. But if we want to understand the work of companies like Netflix, it is not enough to simply conclude that culture and technology — humans and computers — are mixed. The question we need to answer is how."
algorithms  culture  engineering  netflix  nickseaver  anthropology  reverseengineering  alexismadrigal  nicholasdiakopoulos  technology  invention  2014 
march 2014 by robertogreco

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