When Code Is Hot

Excellent article on TechCrunch by Jon Evans, “When Code is Hot”



“That first cited piece above begins with “Parlez-vous Python?”, a cutesy bit that’s also a pet peeve. Non-coders tend to think of different programming languages as, well, different languages. I’ve long maintained that while programming itself — “computational thinking”, as the professor put it — is indeed very like a language, “programming languages” are mere dialects; some crude and terse, some expressive and eloquent, but all broadly used to convey the same concepts in much the same way.

Like other languages, though, or like music, it’s best learned by the young. I am skeptical of the notion that many people who start learning to code in their 30s or even 20s will ever really grok the fundamental abstract notions of software architecture and design.

Stross quotes Michael Littman of Rutgers: “Computational thinking should have been covered in middle school, and it isn’t, so we in the C.S. department must offer the equivalent of a remedial course.” Similarly, the Guardian recently ran an excellent series of articles on why all children should be taught how to code. (One interesting if depressing side note there: the older the students, the more likely it is that girls will be peer-pressured out of the technical arena.)”


First, some news on the Crimson Hexagon front:

Repucom International Selects Crimson Hexagon Social Media Analysis Platform to Augment its Sponsorship Intelligence Services


Second, a great example of what I love about the hard sciences:

Earth Has Just One Moon, Right? Think Again

As social scientists, it is easy to think that what makes the harder sciences so reliable is the math and the equipment, but the truth is that the harder sciences are bolstered by the constant, constructive skepticism.

Don’t fear Big Data

I really enjoyed this RTI blog post about embracing big data:


I suspect that oftentimes fear of big data is motivated by a concern that new, less tested, still evolving methods will replace the time tested methods that we have grown to have so much faith in. I sincerely believe that the foundation that we have is a strong one, and the knowledge we have developed through those processes should be embraced, especially the quality controls. But SUPPLEMENTING an analysis through a measured combination of data sources can lead to a more complete picture.

This week I spent some time analyzing Pew’s report on the Kony 2012 video. I believe that this report is an excellent example of what researchers are capable of when they look outside the artificial divisions of research group (this was a collaborative effort) and research methodology. Seven days after the release of the video, Pew was able to reconstruct a comprehensive narrative of the video’s dissemination, using traditional survey methods, sentiment analytic snapshots over time, and a careful breakdown of the media coverage of influential parties.


Dana Boyd also has an interesting analysis of the Kony phenomena on her Apophenia blog:


Fostering Creativity at Work

This book looks fantastic. Whenever I need to do a lot of thinking at work, I’ll go for a walk or hit the gym. Or start reading about a similar topic. Or stare out the window. We don’t have Ping Pong tables, but we do have floor to ceiling windows overlooking a wooded patch. I can’t tell you how any cumulative hours I’ve spent watching the trees wave in the wind and working my way through a stumbling block.



Why Social Media couldn’t predict Super Tuesday

This piece is a nice reminder not only, as the authors conclude, that sentiment analysis has not fully matured, but also that sentiment analysis and social media analysis probably don’t accomplish what they think they are accomplishing:



Taking it to the source

As a survey researcher at the American Institute of Physics, I often say that I don’t study physics, I study people who study physics. In that respect, this is an interesting way to take it back:


This graphic shows how I owe a debt of gratitude to the physicists I study for making the devices through which I study them.

I guess some would say that sooner or later, it has to come back to physics (physics being the root of all things)!

Seriously, though. It’s an interesting and fun graphic. Check it out.

Telling your story in qualitative and quantitative terms

This is a really interesting piece by Robery Krulwich:


In it, he asks a writer and a physicist to talk about their lives. They describe their lives in vastly different terms. The writers tells his story in narrative form, and the physicist describes his in terms of data. In fact, the physicist has amassed a sizable collection of what he calls ‘personal data.’ He provides an interesting an unusual perspective and begs the question ‘what does this data mean?’ or, as Dr Castillo Ayometzi would ask, “what is being constituted here?”