Amanda Filipacchi has a great post at the New York Times about the problem with classifying American female novelists as American female novelists. That’s been going on at Wikipedia, with the result that the category American novelist was becoming filled predominantly with male novelists.

Part of this is undoubtedly due to the dumb sexism that thinks that “normal” novelists are men, and thus women novelists need to be called out. And even if the category male novelist starts being used, it still assumes that gender is a primary way of dividing up novelists, once you’ve segregated them by nation. Amanda makes both points.

From my point of view, the problem is inherent in hierarchical taxonomies. They require making decisions not only about the useful ways of slicing up the world, but also about which slices come first. These cuts reflect cultural and political values and have cultural and political consequences. They also get in the way of people who are searching with a different way of organizing the topic in mind. In a case like this, it’d be far better to attach tags to Wikipedia articles so that people can search using whatever parameters they need. That way we get better searchability, and Wikipedia hasn’t put itself in the impossible position of coming up with a taxonomy that is neutral to all points of view.

Wikipedia’s categories have been broken for a long time. We know this in the Library Innovation Lab because a couple of years ago we tried to find every article in Wikipedia that is about a book. In theory, you can just click on the “Book” category. In practice, the membership is not comprehensive. The categories are inconsistent and incomplete. It’s just a mess.

It may be that a massive crowd cannot develop a coherent taxonomy because of the differences in how people think about things. Maybe the crowd isn’t massive enough. Or maybe the process just needs far more guidance and regulation. But even if the crowd can bring order to the taxonomy, I don’t believe it can bring neutrality, because taxonomies are inherently political.

There are problems with letting people tag Wikipedia articles. Spam, for example. And without constraints, people can lard up an object with tags that are meaningful only to them, offensive, or wrong. But there are also social mechanisms for dealing with that. And we’ve been trained by the Web to lower our expectations about the precision and recall afforded by tags, whereas our expectations are high for taxonomies.

Go tags.

I’m very proud to announce that the Harvard Library Innovation Lab (which I co-direct) has launched what we think is a useful and appealing way to browse books at scale. This is timed to coincide with the launch today of the Digital Public Library of America. (Congrats, DPLA!!!)

StackLife (nee ShelfLife) shows you a visualization of books on a scrollable shelf, which we turn sideways so you can read the spines. It always shows you books in a context, on the ground that no book stands alone. You can shift the context instantly, so that you can (for example) see a work on a shelf with all the other books classified under any of the categories professional cataloguers have assigned to it.

We also heatmap the books according to various usage metrics (“StackScore”), so you can get a sense of the work’s community relevance.

There are lots more features, and lots more to come.

We’ve released two versions today.

StackLife DPLA mashes up the books in the Digital Public Library of America’s collection (from the Biodiversity Heritage Library) with books from The Internet Archive‘s Open Library and the Hathi Trust. These are all online, accessible books, so you can just click and read them. There are 1.7M in the StackLife DPLA metacollection. (Development was funded in part by a Sprint grant from the DPLA. Thank you, DPLA!)

StackLife Harvard lets you browse the 12.3M books and other items in the Harvard Library systems 73 libraries and off-campus repository. This is much less about reading online (unfortunately) than about researching what’s available.

Here are some links:

StackLife DPLA: http://stacklife-dpla.law.harvard.edu
StackLife Harvard: http://stacklife.law.harvard.edu
The DPLA press release: http://library.harvard.edu/stacklife-browse-read-digital
The DPLA version FAQ: http://stacklife-dpla.law.harvard.edu/#faq/

The StackLife team has worked long and hard on this. We’re pretty durn proud:

Annie Cain
Paul Deschner
Kim Dulin
Jeff Goldenson
Matthew Phillips
Caleb Troughton

I had both CNN and Twitter on yesterday all afternoon, looking for news about the Boston Marathon bombings. I have not done a rigorous analysis (nor will I, nor have I ever), but it felt to me that Twitter put forward more and more varied claims about the situation, and reacted faster to misstatements. CNN plodded along, but didn’t feel more reliable overall. This seems predictable given the unfiltered (or post-filtered) nature of Twitter.

But Twitter also ran into some scaling problems for me yesterday. I follow about 500 people on Twitter, which gives my stream a pace and variety that I find helpful on a normal day. But yesterday afternoon, the stream roared by, and approached filter failure. A couple of changes would help:

First, let us sort by most retweeted. When I’m in my “home stream,” let me choose a frequency of tweets so that the scrolling doesn’t become unwatchable; use the frequency to determine the threshold for the number of retweets required. (Alternatively: simply highlight highly re-tweeted tweets.)

Second, let us mute based on hashtag or by user. Some Twitter cascades I just don’t care about. For example, I don’t want to hear play-by-plays of the World Series, and I know that many of the people who follow me get seriously annoyed when I suddenly am tweeting twice a minute during a presidential debate. So let us temporarily suppress tweet streams we don’t care about.

It is a lesson of the Web that as services scale up, they need to provide more and more ways of filtering. Twitter had “follow” as an initial filter, and users then came up with hashtags as a second filter. It’s time for a new round as Twitter becomes an essential part of our news ecosystem.