recentpopularlog in

robertogreco : datascience   4

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."

[direct link to audio: https://soundcloud.com/mit-cmsw/nick-seaver-what-do-people-do-all-day ]

[via: https://twitter.com/allank_o/status/961382666573561856 ]
nickseaver  2016  work  labor  algorithms  bigdata  music  productivity  automation  care  maintenance  programming  computing  hierarchy  economics  data  datascience 
february 2018 by robertogreco
Refugee Data Tells Visual Stories of a Changing World - Scientific American Blog Network
"Two interactive infographics help users visualize today's global refugee crisis and compare it with similar crises in the past "



""While this visualization limits its scope to Europe, the Middle East, and northern Africa, and only covers 2012-2015, these decisions allow for a remarkable level of granularity in the stories it tells. To leave all filters off and watch the continuous, daily flow of asylum-seekers is mesmeric, if rather overwhelming. But hover over Syria, and the formerly chaotic data becomes powerfully transparent. Moreover, as the slider inches toward September and October of 2015, the dense stream of white pixels becomes not only illuminating, but somehow, oddly moving.

As the current refugee crisis continues to transform lives and challenges more nations to respond—ideally with compassion—this map is bound to shift in new ways every day. I trust that, at the very least, someone will be there to visualize it.
datascience  data  refugees  datavisualization  via:willrichardson  2016  maps  mapping 
january 2016 by robertogreco
Of Data Scientists, Big Data, the City and Dancers « Rev Dan Catt's Blog
"Lefebvre…talks about rhythm of cities…flow of people, morning coffee routine, lunchtime decisions…

How people shape the city, the pulse as agents gather together to form a temporary autonomous zone before collapsing back to being shaped by the city. To be not just in the city, but of the city.

I’m not a fan of cities…can’t design for cities as I don’t understand them.

Lefebvre’s writing suggests that to analyze a city you need to have been consum/mat/ed by it.

…same as Big Data. You can’t have someone who’s a “Data Scientist” just turn up & apply tools, clusters & statistics…haven’t been in-it enough…can’t have someone who’s w/in company, understands & feels flow of data everyday, unless…they know how to separate themselves…get outside. When people grow w/ a company, love it, understand everything that it could be, getting outside it is a hard won skill. “Scientist” needs to be able to remove self & apply clear analytical skill, but w/ fundamental understanding of subject."
revdancatt  cities  fata  henrilefebvre  understanding  urban  urbanism  empathy  objectivity  bigdata  datascience  statistics  programming  2011 
june 2011 by robertogreco

Copy this bookmark:





to read