1——->3——->5
Terrible—->Fantastic
5
Callas, Maria – Rossini and Donizetti Arias [1964]
4
Callas, Maria – Mozart, Beethoven, and Eber [1963]
Tobin, Amon – Isam
Photek – Avalanche
Photek – Aviator EP
Photek – Form & Function, Volume 2
Black Keys – entire discography
3
Callas, Maria – La Gioconda by Amilcare Pochielli [1959]
Callas, Maria – Norma by Vincenzo Bellini
Roots Manuva – 4everevolution
Karen O – Where the Wild Things Grow OST
2
1
Among the things I learned this week:
* Spotify audio quality isn’t as good as I expected, despite knowing the format. I suspect it is a non-issue for most people. (Courtesy: Spotify)
* Weddings benefit from having Baltimore-based attendees: They know how to dance and they play good music. (Courtesy: MLs)
* According to excellent, interesting, insightful, and important research by Byers, Mitzenmacher, and Zervas, daily deals carry a reputational cost to the sponsoring establishment. Specifically, Yelp reviewers who went to an establishment due to a Groupon or LivingSocial coupon, reviewed the establishment 10 percent lower than non-daily deals reviewers. The paper also sheds light and raises questions about the sales dynamics of daily deals and social network effects on these types of sales. I only wish they had better controlled for the Yelp reviews by comparing daily deal establishments with non-daily deal establishments across the same time; not only is this relevant methodologically, but it is also something of interest to me. (Courtesy: Business Insider)
* Reading Rainbow returns as an iOS app! (Courtesy: TUAW)
* Chinese shadow play is cool, but lacks the unmatchable brilliance of gamelan, the music that accompanies Indonesia’s shadow play–wayang kulit. (Courtesy: Tangshan Shadow Puppet Theatre)
* Karen O, like me, is getting into opera. (Courtesy: Yeah Yeah Yeahs)
Many Wikpedia entries contain headings and sub-headings that have temporal connotations. Due to the 2.0 and cross-temporal character of Wikipedia, these headings and sub-headings are ripe for data mining. The question I have is what can we learn from these type of datasets about how humans understand time.
Yeah, yeah, time is a social construct. So is everything. The point is to understand that social construct and how it is constructed. Wikipedia offers a great way of doing so, because the temporal markers are adjusted as extra-Wikipedia time marches. In other words, even as we grow older at a “constant” rate, the temporal markers for a Wikipedia adjust and re-adjust. For example, the Wikipedia article about Wiley has several headings include:
* 1997-2003: Early Years
* 2004-2010: Solo Success
* 2011-present: Recent Work
It’s not difficult to imagine how these three breakdowns of the temporal landscape of his career has evolved since his debut (i.e., it is unlikely that these headings appeared as soon as he made it). So how do these headings evolve over time and what events lead us to impose such periods?
I don’t have an answer, but I sure would like the answer.