I ran across this paper via the
. In general they studied GitHub as a learning community and the social support of people’s friends on the platform as they worked on learning new programming languages.
I think there might be some interesting takeaways for people looking at collective learning and online pedagogies as well as for communities like the IndieWeb which are trying to not only build new technologies, but help to get them into others’ hands by teaching and disseminating some generally tough technical knowledge. (In this respect, the referenced Human Current podcast episode may be a worthwhile overview.)
🔖 The influence of collaboration networks on programming language acquisition by Sanjay Guruprasad | MIT was originally published on Chris Aldrich
📖 Read loc 1-682 of 12932 (5.27%) of American Amnesia by Jacob S. Hacker and Paul Pierson
This portends to be very interesting in that they plan to show what has changed over much of the 1900’s to indicate the drastic evolution in American politics, life, and philosophy over the recent decades. In light of the political battles between the left and the right over the past several years, this could provide some much needed help and guidance.
Their basic thesis seems to be that a shift away from a mixed economy has slowed American growth and general prosperity. While they do seem to have a pointed (political) view, so far it’s incredibly well documented and footnoted for those who would like to make the counter-argument. They’ve definitely got some serious evidence to indicate how drastic the situation is, but I’m curious if they can directly tie their proposed cause to the effect. If nothing else, they’ve created a laundry list of problems in America which need to be addressed by some serious leadership soon.
In some sense I’m torn about what to think of a broader issue this touches upon and which I mentioned briefly while reading At Home in the Universe. Should we continue on the general path we’ve struck out upon (the mixed economy with government regulation/oversight), or should we continue evolving away? While we can’t see the complexity effects seven levels further in, they may be more valuable than what we’ve got now. For example Cesar Hidalgo looks at the evolution along a continuum of personbyte to larger groups: firms (firmbyte), governments, and mega-corporations in Why Information Grows, so I can easily see larger governments and corporations like Google drastically changing the world in which we live (operating at a level above what most humans can imagine presently), but the complexity of why and how they operate above (and potentially against) the good of the individual should certainly be called into question and considered as we move forward.
📖 5.27% done with American Amnesia by Jacob S. Hacker and Paul Pierson was originally published on Chris Aldrich | Boffo Socko
During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.
Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science was originally published on Chris Aldrich