Saturday, May 3, 2014

Weekly resources (3): Objectivity, data journalism, visualization, infographics

It's been a while since the previous resources post, so this is going to be pretty long.


• NYT's The Upshot looked great when it was launched, and it's only getting better. I'm always thrilled when news publications use articles as pretexts to explain statistical concepts. Sampling error and confidence intervals are rarely present in news stories, but they are crucial to understanding quantitative information. Here's a smart example.

• PAUL BRADSHAW's  'Unicorns, racehorses – and a mule cameo: data journalism in 2014' is excellent. Please read it carefully and review the slides that Bradshaw has made available:
While we do need more unicorns (more on this in a future post), we also need journalists who use data journalism techniques to speed up their newsgathering and report stories they might otherwise not, because of time limitations.
• SIMON ROGERS has written about our upcoming MOOC on data journalism and visualization.

• NATE SILVER, interviewed: 
“If you’re an aspiring journalist who knows how to code really well, you are in a very hot market.”

• MU LIN's Teaching data visualization: Recommended readings and resources is a much better curated list than mine.

• STEVE DOIG shares valuable tips on how Getting started in data journalism: the first steps in a story.
A data story normally revolves around a hypothesis, said Doig, and then testing that hypothesis against stated, verified data. To know what data is needed, a journalist must assess what variables are involved – age, sex, location, job, income, crime – and understand which organizations or government bodies collect those variables. (Then, you will need to look for the data, clean it, search for patterns, and analyze them.)
• THE JOURNALISM FESTIVAL in Perugia, Italy, discussed 'Global developments in data journalism.'

• PROPUBLICA's 'Segregation now' is in-depth and data-driven reporting at its best. Don't miss it. More details.


magazine has put The Backlash Against Big Data into perspective. It's a good summary of the debate:
The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem. There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever.
• THE NEW REPUBLIC condemns the false promise of the digital humanities. I'm always wary when this publication spouts anything about science and data, considering its dreadful record, but this is an interesting essay about how humanistic inquiry works. Forget about its sensationalistic headline and focus on the content. Quoting:
If we ask the data unsophisticated or banal questions, we will get only unsophisticated and banal answers.
Indeed. However:
The best thing that the humanities could do at this moment, then, is not to embrace the momentum of the digital, the tech tsunami, but to resist it and to critique it. This is not Luddism; it is intellectual responsibility. Is it actually true that reading online is an adequate substitute for reading on paper? If not, perhaps we should not be concentrating on digitizing our books but on preserving and circulating them more effectively. Are images able to do the work of a complex discourse? (...) These are the kinds of questions that humanists ought to be well equipped to answer.
I fail to see how you can do all that if it's not by taking advantage of data analysis and several branches of the sciences, beginning with cognitive psychology. But I'm sure that Leon Wieseltier, literary editor at The New Republic, would not approve of this. It might be better to endlessly ruminate over how many angels can dance on the head of a pin. Using tons of quotes from Foucault and stuff.

• SALON HAS PUBLISHED a brouhaha piece on Ezra Klein, Nate Silver, and Jonathan Chait. Some of the points made by the author, Eias Isquith, have merit, as it's true that Vox and FiveThirtyEight overstated what they were trying to achieve. Nonetheless, I have doubts about what hums behind Isquith's rhetoric. Here's a revealing paragraph:
These three men aren’t rolling back civil rights or bringing back the Bee Gees, but they’re doing something almost as bad: Whether they know it or not, they’re bringing back what media critic and New York University professor Jay Rosen has famously called “the view from nowhere,” an awful tic of American journalism that I believed, apparently in error, most smart and thoughtful reporters had abandoned after the blogosphere shook up the industry some 10-plus years ago.
Well, no. Leaving the hype aside, what the current —and perhaps temporary— popularity of data and explanatory journalism suggests is that there may be an audience for news publications in which journalists try to be serious about using hard evidence and analysis to curb their biases, rather than embracing them happily. That's hardly a "view from nowhere." More:
The ascension of “partisan” media like Fox News, the Huffington Post, “lean forward”-era MSNBC and group blogs on the left (Daily Kos) and right (RedState) was ultimately a good thing. There were drawbacks to ideological news sources, sure (but) readers could have more of a sense of the biases undergirding any given news source’s reporting and could apply grains of salt accordingly.
This is a much, much more damaging myth than the rusty notion of journalistic objectivity as equivalent to simplistic neutrality. Do you really believe that people systematically "apply grains of salt" to what they read or see? Good luck with that. Transparency is not the new objectivity, but just a part of objectivity. Quoting from a good article about it:
Scientists begin their research with assumptions. They have expectations of what will happen, but they don’t know what will happen. (…) Their objective, scientific inquiry is not one that is without bias, but one in which bias has to stand up to evidence and results. 
This is the sensible and realistic approach to objectivity that might be termed But what, exactly, was objective journalism? Were all-too-human journalists supposed to stop being humans and somehow expunge all the prejudices that they carried inside them? Were they to be objective, meaning that they would approach each new subject like a blank slate without opinions?genuine objectivity. It begins with the assumption that journalists have bias, and that their bias has to be tested and challenged by gathering facts and information that will either support it or knock it down. 
Jay Rosen, quoted in the erratic Salon article, said exactly the same thing in this interview:
We should all want more of them.