Nothing is “Miscellaneous” October 17, 2009Posted by Andre Vellino in Classification, Collaborative filtering, Information retrieval, Recommender service.
I think I now understand why David Weinberger’s book “Everything is Miscellaneous” is so provocative and sometimes enraging. It often sounds like he’s claiming that there is no point at all in classifing / categorizing information. No matter what you do, you’re going to get the category “wrong” because there is no such thing as a “right” category. Ergo, don’t even try – everything belongs in the category “Misc”.
I think Weinberger’s emperor has no clothes – in fact, he is asserting that nothing is “Miscellanous”. Everything belongs to some category for someone, it’s just that it may not be the same category for everyone. A banana is likely to be a fruit for most people, but also a weapon for John Cleese. The point is: a banana is always a kind of something in every context.
So isn’t there is a middle ground between banishing the Dewey decimal system (or indeed any other library classification system) and dumping every digital object into an undifferentiated pile. Indeed, there’s a lot to be said for a thoroughly well-understood standard, albeit a dated and even a bad, system of classification: at the very least, it is predictable. If you know how the meta-data was generated (e.g. call-number, subject category, keywords), for a given item, you’ll be better able to retrieve it.
Furthermore, I expect there are some unforseen problems with the democratization of knowledge generated by social tagging and recommender systems. Who’s doing the tagging? Who’s doing the bookmarking? High school students?
This is of particular concern to me in the context of scholarly articles. Are the numbers of co-downloads in a digital library primarily due to professors’ undergraduate course syllabi? Would professors’ syllabi be influenced by scholarly recommender systems? I expect that the recommender-effect studied in Daniel Fleder’s “Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity” and which shows that recommenders decrease aggregate diversity would be an especially accute problem when sources of co-download behaviour are (relatively) few (e.g. professors’ course syllabi).
Conclusion? I think it matters what population you are drawing from for your metadata – be it social tagging or collaborative filtering recommendations. There is a point in relying on experts and big thinkers. They are more knowledgeable and credible than even the collective intelligence of the masses.