Among the things I learned this week:
* James Garfield was the last president to be born in a log cabin. (Courtesy: Candice Millard)
* A woman, Lyudmila Pavlichenko, was the first Soviet citizen to meet a US president. (Courtesy: Wikipedia)
* In 1994, the United States banned the import of China-made assault weapons. (Courtesy: NYT)
Among the things I learned this week:
* What it’s like to survive the Ring Cycle. (Courtesy: The Met)
* Horse events continue to be fun. (Courtesy: Virginia Gold Cup)
* The history, evolution, and nuances involved in DPRK portraiture. (Courtesy: AsiaTimes)
* DPRK is joining the Paralympics. (Courtesy: Yonhap)
* Sri Lanka has tons of cool historic markers: oldest continuously documented tree, first female ruler in Asia, first dedicated hospital, and the oldest democracy in South Asia. (Courtesy: Wikipedia)
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.