Scoops in Journalism and Everyday Life

Jay Rosen has a post today titled "Four Types of Scoops" that will surely make it into my sociology of information book.  The four types are the "enterprise scoop" where the reporter who gets the scoop gets it by doing the "finding out."  The information may be deliberately hidden or obscured by routine practice, but it would not have become known to the public without the work of the reporter.  Then there is its opposite, the the "ego scoop": the news would have come out anyway, but the scooper gets (or provokes) a tip or equivalent.  The third type Rosen calls the "trader's" scoop where early info has instrumental value -- as in a stock tip.  Finally there is the "thought scoop."  This is when the writer puts two and two together or otherwise "connects the dots" to, as he says, "apprehend--name and frame--something that's happening out there before anyone else recognizes it."

The information order of everyday life is conditioned by information exchanges that might be similarly categorized.  But even before that we'd notice a distinction between exchanges that are NOT experiences as scoops -- I think there are two extremes: information passed on bucket-brigade style with no claim at all to having generated it or deserving any credit for its content or transmission.  "Hey, they've run out of eggs, pass it on, eh?"  and statements of a truly personal nature: "I'm not feeling well today" that do not reflect one's position or location or worth in the world.

Between these there are all manner of instances in which people play the scoop game in everyday interaction.  The difference between an ordinary person and a reporter in this regard is that the reporter's scoop is vis a vis "the rest of the media" while the scoopness of the person's scoop is centered in the information ecology of the recipient.  We have all met the inveterate ego scooper who moves from other to other to other trying to stay one step ahead of the diffusing information so that s/he can deliver the "scoop" over and over.  And the enterprising gossip who pries information loose from friends and acquaintances and is always ready with the latest tidbit.   In everyday interaction the wielder of the traders' scoop often generates the necessary arbitrage because others are willing to "pay" for information they can use as ego scoops.  Alas, as in the media, the thought scoop is probably the rarest form in everyday life too.  It's probably less self-conscious in everyday interaction and too more ephemeral which is too bad.  Those conversational insights are probably more often lost than their counterparts in "print."

Tomorrow's Social Science Today? By Techies?

If you generate the data, the analysts will come.  And more and more of the technologies of everyday life generate data, lots of it. "Big data" takes big tools and big tools cost big bucks.  The science of big data is mostly social science but, for the most part, it's not being done by social scientists.  What's left out when social scientists leave themselves out of the conversation? And what happens to the funding for non-big-data social science when resource-hungry projects like this emerge?  And what will be the effect on the epistemological status of non-big-data social science?

from the New York Times...

THE BAY CITIZEN

Berkeley Group Digs In to Challenge of Making Sense of All That Data

By JEANNE CARSTENSEN

Published: April 7, 2012

"It comes in “torrents” and “floods” and threatens to “engulf” everything that stands in its path.

No, it is not a tsunami, it is Big Data, the incomprehensibly large amount of raw, often real-time data that keeps piling up faster and faster from scientific research, social media, smartphones — virtually any activity that leaves a digital trace.

The sheer size of the pile (measured in petabytes, one million gigabytes, or even exabytes, one billion gigabytes) combined with its complexity has threatened to overwhelm just about everybody, including the scientists who specialize in wrangling it. “It’s easier to collect data,” said Michael Franklin, a professor of computer science at the University of California, Berkeley, “and harder to make sense of it.”

Read full article...