Think of a copy editor as a parent trying to clean up a teenager's room. You open the door and, God above, there are discarded articles of clothing on every surface. You start to dig in and discover dirty plates, some with unconsumed food on them; notes and uncompleted homework assignments; still more malodorous articles of clothing, along with the unspeakable sheets; and, under the bed, dust bunnies the size of tumbleweeds.
The blunt truth is that most people, and that can include many academics, are not very good writers. Their prose needs the basic cleaning up, but it also needs the clarification, the sharpening and pruning. The sad truth is that many professional writers are not particularly good at it either, and I can speak from the experience of one who has dealt with the prose of hundreds of professional journalists. As my former colleague Rafael Alvarez once said after a stint on the metro desk, "Reading other people's raw copy is like looking at your grandmother naked."
Read more at the Baltimore Sun
Friday, January 4
Thursday, January 3
Key Academic Research Resources
A key skill is the ability to locate and review academic studies to strengthen and deepen stories. One common search strategy for finding academic research is trying a series of keywords in popular search engines such as Google, Yahoo and Bing. That general method may fail if you’re trying to find cutting-edge research findings on policy or news-related issues. While no particular strategy is perfect, establishing a checklist of key databases is essential. Your selection of databases may ultimately need to be tailored and subject-specific, but it helps to have familiarity with a basic, multidisciplinary set of research tools — a “go-to” set of databases. Using your keywords systematically through a series of databases can diversify your search and allow you to locate most of the best available research.
Read more at Journalists' Resources
Read more at Journalists' Resources
10 things every journalist should know in 2013
Here are 10 things every journalists should know in 2013.
1. It's all about skills, skills, skills
Aron Pilhofer, editor of interactive news at the New York Times, has one piece of advice for journalists wanting to get ahead: "Skills, skills, skills, skills, skills, skills."
"Unfortunately or fortunately, depending on how you look at it, it is just not enough any more to just be able to turn a phrase, or do the traditional kinds of reporting," he told Journalism.co.uk. "You need to be a little bit of a jack of all trades; you need to be able to shoot and cut video or do audio or code or do data analysis," he said.
"And it's even more important now than it ever has been in this shrinking industry to have those kinds of skills."
Steve Herrmann, editor of BBC News online, agrees. He looks for those with skills in social media, data journalism and with "an ability to appreciate the importance of still pictures, of video, graphics and audio in communicating and telling stories".
"It's not necessarily being expert in all of those things," Herrmann said, "but being aware of their importance and appreciating when they can be really effective and have impact."
9. Online journalism is mobile first
Mobile is an important traffic driver for news sites, we are regularly reminded. At particular times of the day the Guardian's mobile traffic exceeds desktop traffic; on an average weekday 24 per cent of readers of BBC News access via mobile, with that rising to a record of 30 per cent on the day of the US election last November, the BBC Editors' blog reported. And more than a third of New York Times traffic now comes from phones and tablets, according to this post by Martin Belam.
But while some sites have a mobile first strategy when thinking about how to present data visualisations, features and multimedia, many journalists have desktop in mind when creating the story.
In a presentation at December's news:rewired digital journalism conference, Belam said: "Think reader before editor. Think software before content. Think simplicity before features. Think mobile before desktop."
Read more here
1. It's all about skills, skills, skills
Aron Pilhofer, editor of interactive news at the New York Times, has one piece of advice for journalists wanting to get ahead: "Skills, skills, skills, skills, skills, skills."
"Unfortunately or fortunately, depending on how you look at it, it is just not enough any more to just be able to turn a phrase, or do the traditional kinds of reporting," he told Journalism.co.uk. "You need to be a little bit of a jack of all trades; you need to be able to shoot and cut video or do audio or code or do data analysis," he said.
"And it's even more important now than it ever has been in this shrinking industry to have those kinds of skills."
Steve Herrmann, editor of BBC News online, agrees. He looks for those with skills in social media, data journalism and with "an ability to appreciate the importance of still pictures, of video, graphics and audio in communicating and telling stories".
"It's not necessarily being expert in all of those things," Herrmann said, "but being aware of their importance and appreciating when they can be really effective and have impact."
9. Online journalism is mobile first
Mobile is an important traffic driver for news sites, we are regularly reminded. At particular times of the day the Guardian's mobile traffic exceeds desktop traffic; on an average weekday 24 per cent of readers of BBC News access via mobile, with that rising to a record of 30 per cent on the day of the US election last November, the BBC Editors' blog reported. And more than a third of New York Times traffic now comes from phones and tablets, according to this post by Martin Belam.
But while some sites have a mobile first strategy when thinking about how to present data visualisations, features and multimedia, many journalists have desktop in mind when creating the story.
In a presentation at December's news:rewired digital journalism conference, Belam said: "Think reader before editor. Think software before content. Think simplicity before features. Think mobile before desktop."
Read more here
What The Tech World Looks Like To A Teen
Instagram
Looking at her Instagram feed, I noticed that the vast majority of photos were of people – not beautiful views, objects, or experiences. This is in stark contrast to what the people I follow on Instagram take photos of, and very analogous to the photos that appear in my Facebook Newsfeed.
My takeaway: Facebook was smart to buy Instagram.
Facebook
She mentioned that she tries to visit Facebook as infrequently as possible. “It’s addicting,” she bemoaned, “you end up getting lost in it and I don’t like that.” I found this perspective interesting. Facebook is clearly doing a good job delivering relevant content, yet its users (at least this one) feel poorly when they use the service. Related, she mentioned that she only visits Facebook after her Instagram Feed updates have been exhausted.
My takeaway: Facebook may have an irreversibly bad brand.
Read more at BuzzFeed
Looking at her Instagram feed, I noticed that the vast majority of photos were of people – not beautiful views, objects, or experiences. This is in stark contrast to what the people I follow on Instagram take photos of, and very analogous to the photos that appear in my Facebook Newsfeed.
My takeaway: Facebook was smart to buy Instagram.
She mentioned that she tries to visit Facebook as infrequently as possible. “It’s addicting,” she bemoaned, “you end up getting lost in it and I don’t like that.” I found this perspective interesting. Facebook is clearly doing a good job delivering relevant content, yet its users (at least this one) feel poorly when they use the service. Related, she mentioned that she only visits Facebook after her Instagram Feed updates have been exhausted.
My takeaway: Facebook may have an irreversibly bad brand.
Read more at BuzzFeed
Sunday, December 30
Sure, Big Data Is Great. But So Is Intuition
It was the bold title of a conference this month at the Massachusetts Institute of Technology, and of a widely read article in The Harvard Business Review last October: “Big Data: The Management Revolution.” At the M.I.T. conference, a panel was asked to cite examples of big failures in Big Data. No one could really think of any. Soon after, though, Roberto Rigobon could barely contain himself as he took to the stage. Mr. Rigobon, a professor at M.I.T.’s Sloan School of Management, said that the financial crisis certainly humbled the data hounds. “Hedge funds failed all over the world,” he said.
The problem is that a math model, like a metaphor, is a simplification. This type of modeling came out of the sciences, where the behavior of particles in a fluid, for example, is predictable according to the laws of physics.
In so many Big Data applications, a math model attaches a crisp number to human behavior, interests and preferences. The peril of that approach, as in finance, was the subject of a recent book by Emanuel Derman, a former quant at Goldman Sachs and now a professor at Columbia University. Its title is “Models. Behaving. Badly.”
Claudia Perlich, chief scientist at Media6Degrees, an online ad-targeting start-up in New York, puts the problem this way: “You can fool yourself with data like you can’t with anything else. I fear a Big Data bubble.” She is worried about a rush of people calling themselves “data scientists,” doing poor work and giving the field a bad name. Indeed, Big Data does seem to be facing a work-force bottleneck. “We can’t grow the skills fast enough,” says Ms. Perlich.
“Models do not just predict, but they can make things happen. That’s not discussed generally in our field.” Models can create what data scientists call a behavioral loop. A person feeds in data, which is collected by an algorithm that then presents the user with choices, thus steering behavior.
Personally, my bigger concern is that the algorithms that are shaping my digital world are too simple-minded, rather than too smart.
Read more at the New York Times
Claudia Perlich, chief scientist at Media6Degrees, an online ad-targeting start-up in New York, puts the problem this way: “You can fool yourself with data like you can’t with anything else. I fear a Big Data bubble.” She is worried about a rush of people calling themselves “data scientists,” doing poor work and giving the field a bad name. Indeed, Big Data does seem to be facing a work-force bottleneck. “We can’t grow the skills fast enough,” says Ms. Perlich.
“Models do not just predict, but they can make things happen. That’s not discussed generally in our field.” Models can create what data scientists call a behavioral loop. A person feeds in data, which is collected by an algorithm that then presents the user with choices, thus steering behavior.
Personally, my bigger concern is that the algorithms that are shaping my digital world are too simple-minded, rather than too smart.
Read more at the New York Times
What is Big Data? Research roundup, reading list
Data can be text and numbers but can also include maps and images. An array of machines — from underwater sensors and pet collars to mobile phones and traffic signals — can capture reams of data waiting to be sliced, diced and analyzed. In recent years, technological advances have expanded the types of Big Data that can be harnessed and stored — and who has access to these data.
Proponents see it as enabling new businesses and promoting transparency in markets and government. Detractors fear that this transparency will extend into the personal realm, as was the case when Target’s data crunchers correctly determined from shopping patterns that a teenager was pregnant before she had disclosed her condition to family members — or the store.
Big Data involves not only individuals’ digital footprints (data they themselves leave behind) but, perhaps more importantly, also individuals’ data shadows (information about them generated by others).
Read more at Journalists Resources
Proponents see it as enabling new businesses and promoting transparency in markets and government. Detractors fear that this transparency will extend into the personal realm, as was the case when Target’s data crunchers correctly determined from shopping patterns that a teenager was pregnant before she had disclosed her condition to family members — or the store.
Big Data involves not only individuals’ digital footprints (data they themselves leave behind) but, perhaps more importantly, also individuals’ data shadows (information about them generated by others).
Read more at Journalists Resources
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