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July 29 2013

Psychology and the Prevention of War Trauma : An Article Rejected by American Psychologist by Marc…

Psychology and the Prevention of War Trauma: An Article Rejected by American Psychologist
by Marc Pilisuk and Ines-Lena Mahr http://www.projectcensored.org/wp-content/uploads/2013/07/Psych-Prevention-of-War-Trauma-Revised.pdf

There is more than one #narrative that guides the services provided by psychology to the military and its soldiers. The dominant narrative is that wars happen and that a peaceful but powerful nation such as the United States responds to the aggression of other nations or groups using military force when diplomacy or other efforts at persuasion are not successful. This view presumes decisions to engage in war emanate from decisions by democratically elected officeholders to protect us. War requires a great mobilization of technology, supplies and soldiers. Soldiers are recruited for such patriotic service and undergo serious physical and mental challenges, some continuing long after the time of service. Within this framework the sacrifices are justified and the building of psychological resilience for soldiers—as described in an entire issue of the #American_Psychologist dedicated to #Comprehensive_Fitness_Training makes perfect sense. “The program’s overall goal is to increase the number of soldiers who grow through their combat experience and return home without serious mental health problems” according to Michael Matthews, a professor with the Department of Behavioral Sciences and Leadership at the United States Military Academy at West Point.

There is however another narrative that casts the contributions and responsibilities of psychology to the military in a different light. In this perspective violent eruptions occur because some people are deprived or displaced and see no non-violent options to improve the quality of their lives. They see control over the resources needed to make their lives better as increasingly centered among a relatively small group of brokers of concentrated power and wealth. It is the decisions of this elite group, according to this second narrative, that necessitate violence and suggest a common root underlying war, poverty and environmental destruction. Resource depletion now causes or intensifies most overt conflicts, and serious global malnutrition affects 925 million people. Such structural violence is neither accidental nor inevitable. Rather it is, in this narrative, a natural consequence of a system inordinately influenced by a small, interconnected network of corporate, military, and government leaders with the power to instill fear, to increase their excessive fortunes, and to restrict information, particularly about their own clandestine dealings. With the predictable benefits of violence going to a small set of corporate and government officials, the recruitment and motivation of soldiers, and of the public, requires a measure of concealment or deception as to who will pay what costs and who will receive what benefits. In this view the sacrifices required from soldiers not only go well beyond what resilience training may prevent, but are not justifiable in the first place. This second narrative calls psychologists to different tasks. They are to draw attention to voices that have been excluded, to clarify the deep psychological and social consequences of the dominant narrative, and to illustrate for people who have been adversely affected, the ways to resolve conflicts without recourse to killing.

The resilience training program flags a larger concern that the discipline of psychology needs to come to grips with the implications of its involvement in facilitating the psychological preparation for war.

#psychologie #états-unis

June 15 2012

Top Stories: June 11-15, 2012

Here's a look at the top stories published across O'Reilly sites this week.

A reduced but important future for desktop computing
Josh Marinacci says most people will rely on mobile devices to handle their computing needs, but a select and small group of power users will continue to use desktop machines.

Big ethics for big data
"Ethics of Big Data" authors Kord Davis and Doug Patterson explore ownership, anonymization, privacy, and ways to evaluate and establish ethical data practices within an organization.

Stories over spreadsheets
Imagine a future where clear language supplants spreadsheets. In a recent interview, Narrative Science CTO Kris Hammond explained how we might get there.


Data in use from public health to personal fitness
Releasing public data can't fix the health care system by itself, but it provides tools as well as a model for data sharing.


What is DevOps?
NoOps, DevOps — no matter what you call it, operations won't go away. Ops experts and development teams will jointly evolve to meet the challenges of delivering reliable software to customers.


Velocity 2012: Web Operations & Performance — The smartest minds in web operations and performance are coming together for the Velocity Conference, being held June 25-27 in Santa Clara, Calif. Save 20% on registration with the code RADAR20.

June 14 2012

Stories over spreadsheets

I didn't realize how much I dislike spreadsheets until I was presented with a vision of the future where their dominance isn't guaranteed.

That eye-opening was offered by Narrative Science CTO Kris Hammond (@whisperspace) during a recent interview. Hammond's company turns data into stories: They provide sentences and paragraphs instead of rows and columns. To date, much of the attention Narrative Science has received has focused on the media applications. That's a natural starting point. Heck, I asked him about those very same things when I first met Hammond at Strata in New York last fall. But during our most recent chat, Hammond explored the other applications of narrative-driven data analysis.

"Companies, God bless them, had a great insight: They wanted to make decisions based upon the data that's out there and the evidence in front of them," Hammond said. "So they started gathering that data up. It quickly exploded. And they ended up with huge data repositories they had to manage. A lot of their effort ended up being focused on gathering that data, managing that data, doing analytics across that data, and then the question was: What do we do with it?"

Hammond sees an opportunity to extract and communicate the insights locked within company data. "We'll be the bridge between the data you have, the insights that are in there, or insights we can gather, and communicating that information to your clients, to your management, and to your different product teams. We'll turn it into something that's intelligible instead of a list of numbers, a spreadsheet, or a graph or two. You get a real narrative; a real story in that data."

My takeaway: The journalism applications of this are intriguing, but these other use cases are empowering.

Why? Because most people don't speak fluent "spreadsheet." They see all those neat rows and columns and charts, and they know something important is tucked in there, but what that something is and how to extract it aren't immediately clear. Spreadsheets require effort. That's doubly true if you don't know what you're looking for. And if data analysis is an adjacent part of a person's job, more effort means those spreadsheets will always be pushed to the side. "I'll get to those next week when I've got more time ..."

We all know how that plays out.

But what if the spreadsheet wasn't our default output anymore? What if we could take things most of us are hard-wired to understand — stories, sentences, clear guidance — and layer it over all that vital data? Hammond touched on that:

"For some people, a spreadsheet is a great device. For most people, not so much so. The story. The paragraph. The report. The prediction. The advisory. Those are much more powerful objects in our world, and they're what we're used to."

He's right. Spreadsheets push us (well, most of us) into a cognitive corner. Open a spreadsheet and you're forced to recalibrate your focus to see the data. Then you have to work even harder to extract meaning. This is the best we can do?

With that in mind, I asked Hammond if the spreadsheet's days are numbered.

"There will always be someone who uses a spreadsheet," Hammond said. "But, I think what we're finding is that the story is really going to be the endpoint. If you think about it, the spreadsheet is for somebody who really embraces the data. And usually what that person does is they reduce that data down to something that they're going to use to communicate with someone else."

A thought on dashboards

I used to view dashboards as the logical step beyond raw data and spreadsheets. I'm not so sure about that anymore, at least in terms of broad adoption. Dashboards are good tools, and I anticipate we'll have them from now until the end of time, but they're still weighed down by a complexity that makes them inaccessible.

It's not that people can't master the buttons and custom reports in dashboards; they simply don't have time. These people — and I include myself among them — need something faster and knob-free. Simplicity is the thing that will ultimately democratize data reporting and data insights. That's why the expansion of data analysis requires a refinement beyond our current dashboards. There's a next step that hasn't been addressed.

Does the answer lie in narrative? Will visualizations lead the way? Will a hybrid format take root? I don't know what the final outputs will look like, but the importance of data reporting means someone will eventually crack the problem.

Full interview

You can see the entire discussion with Hammond in the following video.

Related:

March 08 2012

Profile of the Data Journalist: The Storyteller and The Teacher

Around the globe, the bond between data and journalism is growing stronger. In an age of big data, the growing importance of data journalism lies in the ability of its practitioners to provide context, clarity and, perhaps most important, find truth in the expanding amount of digital content in the world. In that context, data journalism has profound importance for society.

To learn more about the people who are doing this work and, in some cases, building the newsroom stack for the 21st century, I conducted in-person and email interviews during the 2012 NICAR Conference and published a series of data journalist profiles here at Radar.

Sarah Cohen (@sarahduke), the Knight professor of the practice of journalism and public policy at Duke University, and Anthony DeBarros (@AnthonyDB), the senior database editor at USA Today, were both important sources of historical perspective for my feature on how data journalism is evolving from "computer-assisted reporting" (CAR) to a powerful Web-enabled practice that uses cloud computing, machine learning and algorithms to make sense of unstructured data.

The latter halves of our interviews, which focused upon their personal and professional experience, follow.

What data journalism project are you the most proud of working on or creating?

DeBarros: "In 2006, my USA TODAY colleague Robert Davis and I built a database of 620 students killed on or near college campuses and mined it to show how freshmen were uniquely vulnerable. It was a heart-breaking but vitally important story to tell. We won the 2007 Missouri Lifestyle Journalism Awards for the piece, and followed it with an equally wrenching look at student deaths from fires."

Cohen: "I'd have to say the Pulitzer-winning series on child deaths in DC, in which we documented that children were dying in predictable circumstances after key mistakes by people who knew that their agencies had specific flaws that could let them fall through the cracks.

I liked working on the Post's POTUS Tracker and Head Count. Those were Web projects that were geared at accumulating lots of little bits about Obama's schedule and his appointees, respectively, that we could share with our readers while simultaneously building an important dataset for use down the road. Some of the Post's Solyndra and related stories, I have heard, came partly from studying the president's trips in POTUS Tracker.

There was one story, called "Misplaced Trust," on DC's guardianship system, that created immediate change in Superior Court, which was gratifying. "Harvesting Cash," our 18-month project on farm subsidies, also helped point out important problems in that system.

The last one, I'll note, is a piece of a project I worked on, in which the DC water authority refused to release the results of a massive lead testing effort, which in turn had shown widespread contamination. We got the survey from a source, but it was on paper.

After scanning, parsing, and geocoding, we sent out a team of reporters to neighborhoods to spot check the data, and also do some reporting on the neighborhoods. We ended up with a story about people who didn't know what was near them.

We also had an interesting experience: the water authority called our editor to complain that we were going to put all of the addresses online -- they felt that it was violating peoples' privacy, even though we weren't identifyng the owners or the residents. It was more important to them that we keep people in the dark about their blocks. Our editor at the time, Len Downie, said, "you're right. We shouldn't just put it on the Web." He also ordered up a special section to put them all in print.

Where do you turn to keep your skills updated or learn new things?

Cohen: "It's actually a little harder now that I'm out of the newsroom, surprisingly. Before, I would just dive into learning something when I'd heard it was possible and I wanted to use it to get to a story. Now I'm less driven, and I have to force myself a little more. I'm hoping to start doing more reporting again soon, and that the Reporters' Lab will help there too.

Lately, I've been spending more time with people from other disciplines to understand better what's possible, like machine learning and speech recognition at Carnegie Mellon and MIT, or natural language processing at Stanford. I can't DO them, but getting a chance to understand what's out there is useful. NewsFoo, SparkCamp and NICAR are the three places that had the best bang this year. I wish I could have gone to Strata, even if I didn't understand it all."

DeBarros: For surveillance, I follow really smart people on Twitter and have several key Google Reader subscriptions.

To learn, I spend a lot of time training after work hours. I've really been pushing myself in the last couple of years to up my game and stay relevant, particularly by learning Python, Linux and web development. Then I bring it back to the office and use it for web scraping and app building.

Why are data journalism and "news apps" important, in the context of the contemporary digital environment for information?

Cohen: "I think anything that gets more leverage out of fewer people is important in this age, because fewer people are working full time holding government accountable. The news apps help get more eyes on what the government is doing by getting more of what we work with and let them see it. I also think it helps with credibility -- the 'show your work' ethos -- because it forces newsrooms to be more transparent with readers / viewers.

For instance, now, when I'm judging an investigative prize, I am quite suspicious of any project that doesn't let you see each item, I.e., when they say, "there were 300 cases that followed this pattern," I want to see all 300 cases, or all cases with the 300 marked, so I can see whether I agree.

DeBarros: "They're important because we're living in a data-driven culture. A data-savvy journalist can use the Twitter API or a spreadsheet to find news as readily as he or she can use the telephone to call a source. Not only that, we serve many readers who are accustomed to dealing with data every day -- accountants, educators, researchers, marketers. If we're going to capture their attention, we need to speak the language of data with authority. And they are smart enough to know whether we've done our research correctly or not.

As for news apps, they're important because -- when done right -- they can make large amounts of data easily understood and relevant to each person using them."

These interviews were edited and condensed for clarity.

November 03 2011

How I automated my writing career

In 2001, I got an itch to write a book. Like many people, I naïvely thought, "I have a book or two in me," as if writing a book is as easy as putting pen to paper. It turns out to be very time consuming, and that's after you've spent countless hours learning and researching and organizing your topic of choice. But I marched on and wrote or co-wrote 10 books in a five-year period. I'm a glutton for punishment.

My day job during that time was programming. I've been programming for 16 years. My whole career I've focused on automating the un-automatable — essentially making computers do things people never thought they could do. By the time I started on my 10th book, I got another kind of itch — I wanted to automate my writing career. I was getting bored with the tedium of writing books, and the money wasn't that good.

But that's absurd, right? How can a computer possibly write something coherent and informative, much less entertaining? The "how can a computer possibly do X?" questions are the ones I've spent my career trying to answer. So, I set out on a quest to create software that could write. It took more effort than writing 10 books put together, but after building a team of 12 people, we were able to use our software to generate more than 100,000 sports-related stories in a nine-month period.

Before I get into specifics with what our software produces, I think it's worth highlighting some of the attributes that make software a great candidate to be a writer:

  • Software doesn't get writer's block, and it can work around the clock.
  • Software can't unionize or file class-action lawsuits because we don't pay enough (like many of the content farms have had to deal with).
  • Software doesn't get bored and start wondering how to automate itself.
  • Software can be reprogrammed, refactored and improved — continuously.
  • Software can benefit from the input of multiple people. This is unlike traditional writing, which tends to be a solitary event (+1 if you count the editor).
  • Perhaps most importantly, software can access and analyze significantly more data than what a single person (or even a group of people) can do on their own.

Software isn't a panacea, though. Not all content can be easily automated (yet). The type of content my company, Automated Insights, has automated is quantitatively oriented. That's the trick. We've automated content by applying meaning to numbers, to data. Sports was the first category we tackled. Sports by their nature are very data heavy. By our internal estimates, 70% of all sports-related articles are analyzing numbers in one form or another.

Our technology combines a large database of structured data, a real-time feed of stats, and a large database of phrases, and algorithms to tie it all together to produce articles from two to eight paragraphs in length. The algorithms look for interesting patterns in the data to determine what to write about.

In November of 2010, we launched the StatSheet Network, a collection of 345 websites (one for every Division-I NCAA Basketball team) that were fully automated. Check out my favorite team: UNC Tar Heels.

Automated game recap
Software mines data to construct short game recaps. (Click to see full story.)

We included the typical kind of stats you'd expect on a basketball site, but also embedded visualizations and our fully automated articles. We automated 14 different types of stories, everything from game recaps and previews to players of the week and historical retrospectives. Recently, we launched similar sites for every MLB team (check out the Detroit Tigers site), and soon we are launching sites for every NFL and NCAA Football team.

Sports is only one of many different categories we are working on. We've also done work in finance, real estate and a few other data-intensive industries. However, don't limit your thinking on what's possible. We get a steady stream of requests from non-obvious industries, such as pharmaceutical clinical trials and even domain name registrars. Any area that has large datasets where people are trying to derive meaning from the data are potential candidates for our technology.

Automation plus human, not automation versus human

Creating software that can write long-form narratives is very difficult, full of all sorts of interesting artificial intelligence, machine learning and natural language problems. But with the right mix of talent (and funding), we've been able to do it. It really does take a keen understanding of how software and the written word can work together.

I often hear it suggested that software-generated prose must be very bland and stilted. That's only the case if the folks behind the software write bland and stilted prose. Software can be just as opinionated as any writer.

A common, and funny, question I get from journalists is: "when will you automate me out out of a job?" I find the question humorous because built into the question is the assumption that if our software can write the perfect story on a particular topic, then no one else should attempt to write about it. That's just not going to happen. What's happening instead is that media companies are using our software to help scale their businesses. Initially, that takes the form of generating stories on topics a media outlet didn't have the resources to cover. In other cases, it means putting our stories through an editorial process that customizes the content to the specific needs of the publisher. You still need humans for that. There will be less of a need for folks to spend their time writing purely quantitative pieces, but that should be liberating. Now, they can focus on more qualitative, value-added commentary that humans are inherently good at. Quantitative stories can — and probably should — be mostly automated because computers are better at that.

Software will make hyperlocal content possible and even profitable. Many companies have tried to solve the "hyperlocal problem" with minimal success. It's just too hard to scale content creation out to every town in the U.S. (or the world, for that matter). For certain categories (e.g. high school sports), software-generated content makes perfect sense. You'll see automated content play a big role here in the coming years.

Strata 2012 — The 2012 Strata Conference, being held Feb. 28-March 1 in Santa Clara, Calif., will offer three full days of hands-on data training and information-rich sessions. Strata brings together the people, tools, and technologies you need to make data work.

Save 20% on registration with the code RADAR20

Software-generated books?

Because I've been so focused on running Automated Insights, I haven't had time to write any new books recently. I suggested to a colleague that we should turn our software loose and have it write my next book. He looked at me and asked, "How can it possibly do that?" That's what I like to hear.

But is a software-generated book even feasible? Our software can create eight paragraphs now, but is it possible to create eight chapters' worth of content? The answer is "yes," but not quite the same kind of technical books I used to write, at least right now. It would be easy for us to extend our technology to write even longer pieces. That's not the issue. Our software is good at quantitative analysis using structured data.

The kind of books I used to write were not based on data and were qualitative in nature. I pulled from my experience and did supplemental research, made a judgment on the best way to perform a task, then documented it. We are in the early stages of building software that will do more qualitative analysis such as this, but that's a much harder challenge. The main advantage of today's usage of software writing is to automate repetitive types of content. This is less applicable for books.

In the near term, the writers at O'Reilly and elsewhere have nothing to worry about. But I wouldn't count out automation in the long term.

Associated box score photo on home and category pages via Wikipedia.

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