e-Xploration
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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
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Rescooped by luiy from The New Global Open Public Sphere
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Essay of the Day: #CollectiveIntelligence and Neutral Point of View in the Case of Wikipedia I #CI


Via Pierre Levy
luiy's insight:

We examine whether collective intelligence helps achieve a neutral point of view (NPOV) using data from Wikipedia’s articles on US politics. Our null hypothesis builds on Linus’ Law, often expressed as “Given enough eyeballs, all bugs are shallow.” Our findings are consistent with a narrow interpretation of Linus’ Law, namely, a greater number of contributors to an article makes an article more neutral. No evidence supports a broad interpretation of Linus’ Law. Moreover, several empirical facts suggest the law does not shape many articles. The majority of articles receive little attention, and most articles change only mildly from their initial slant. Our study provides the first empirical evidence on the limit of collective intelligence. While many managers believe that they could improve their products by taking advantage of the wisdom of crowds, we show that in the case of Wikipedia, there are aspects such as NPOV that collective intelligence does not help achieve successfully.

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Frederic DOMON's curator insight, November 17, 2013 2:28 PM

Our study provides the first empirical evidence on the limit of collective intelligence. While many managers believe that they could improve their products by taking advantage of the wisdom of crowds, we show that in the case of Wikipedia, there are aspects such as NPOV that collective intelligence does not help achieve successfully.

Paz Barceló's curator insight, November 18, 2013 6:18 AM

Sobre los límites de la inteligencia colectiva.

Rick Frank's curator insight, November 18, 2013 9:34 AM

Interesting idea, but OMG this is boring to read, needs some STYLE.

Rescooped by luiy from Literacy in the algorithmic medium
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Wikipediocracy

Wikipediocracy | e-Xploration | Scoop.it
A critical review site examining Wikipedia's flaws and follies

Via Pierre Levy
luiy's insight:
Our Mission:We exist to shine the light of scrutiny into the dark crevices of Wikipedia and its related projects; to examine the corruption there, along with its structural flaws; and to inoculate the unsuspecting public against the torrent of misinformation, defamation, and general nonsense that issues forth from one of the world’s most frequently visited websites, the “encyclopedia that anyone can edit.”
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Pierre Levy's curator insight, May 2, 2013 4:56 PM

The dark side of Wikipedia

Rescooped by luiy from Big Data Analysis in the Clouds
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Why Big Data Is Not Truth : “Big Data fundamentalism" | #bigdata #controverses

Why Big Data Is Not Truth : “Big Data fundamentalism" | #bigdata #controverses | e-Xploration | Scoop.it
Don’t let the rhetoric fool you, a Microsoft researcher says: Big Data is a human tool, which means it is subject to all kinds of miscollection, misapplication and abuse.

Via Pierre Levy
luiy's insight:

Kate Crawford, a researcher at Microsoft Research, calls the problem :


“Big Data fundamentalism"


— the idea with larger data sets, we get closer to objective truth.” Speaking at a conference in Berkeley, Calif., on Thursday, she identified what she calls “six myths of Big Data.”

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Intriguing Networks's curator insight, June 2, 2013 4:56 AM

It still is all in the interpretation...

Rescooped by luiy from The Rise of the Algorithmic Medium
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Governing Algorithms: A Provocation Piece

Governing Algorithms: A Provocation Piece | e-Xploration | Scoop.it
Algorithms have developed into somewhat of a modern myth. They “compet[e] for our living rooms” (Slavin 2011), “determine how a billion plus people get where th

Via Pierre Levy
luiy's insight:

Abstract:      
Algorithms have developed into somewhat of a modern myth. They “compet[e] for our living rooms” (Slavin 2011), “determine how a billion plus people get where they’re going” (McGee 2011), “have already written symphonies as moving as those composed by Beethoven” (Steiner 2012), and “free us from sorting through multitudes of irrelevant results” (Spring 2011). Nevertheless, the nature and implications of such orderings are far from clear. What exactly is it that algorithms “do”? What is the role attributed to “algorithms” in these arguments? How can we turn the “problem of algorithms” into an object of productive inquiry? This paper sets out to trouble the coherence of the algorithm as an analytical category and explores its recent rise in scholarship, policy, and practice through a series of provocations.

 

Keywords: algorithms, governance, automation, computation, big data, sociology, law, public policy, control

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Pierre Levy's curator insight, April 20, 2013 2:40 PM

governance, automation, computation, big data, sociology, law, public policy, control