Recent work: NY Times’ 9-year-old terror error; local news ethics; Wikipedia

Sometimes your labor on a bunch of projects comes to fruition all at once. Here are some links to recently published stuff:

Corrections in the Web Age: The Case of the New York Times’ Terror Error — How did a 2002 error in the New York Times wreck a KQED interview in 2011 about John Walker Lindh, the “American Taliban”? And what does the incident tell us about how newsroom traditions of verification and correction must evolve in the digital age? MediaBugs’ Mark Follman and I put together this case study and it’s all here in the Atlantic’s fantastic Tech section. If you’re wondering what the point of MediaBugs is or why I’ve spent so much of the past two years working on it, this is a good summary!

Rules of the Road: Navigating the New Ethics of Local Journalism: I spent a considerable amount of time last winter and spring interviewing a whole passel of editors and proprietors of local news sites as part of this project for JLab, trying to find the tough questions and dilemmas they face as old-fashioned journalism ethics collide with the new shapes local journalism is taking online. It was a blast doing the interviews and fun assembling the results with Andy Pergam, Jan Schaffer and everyone else at JLab. It’s all on the website but it’s also available in PDF and print.

Whose point of view?: In the American Prospect, I used Wikipedia’s article on Social Security as an example to explore how Wikipedia’s principle of “neutral point of view” can break down. Here’s an excerpt:

Wikipedia says virtually nothing about the system’s role as a safety net, its baseline protections against poverty for the elderly and the disabled, its part in shoring up the battered foundations of the American middle class, or its defined-benefit stability as a bulwark against the violent oscillations of market-based retirement piggy banks.

This is a problem—not just for Social Security’s advocates but for Wikipedia itself, which has an extensive corpus of customs and practices intended to root out individual bias.

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