Clay Shirky posted an interesting piece a while back declaring “Ontology is Overrated”. I’ve only just seen it (thanks Ben) but it struck a number of chords. Clay suggests that many of the present classification systems such as the Library of Congress system were under some degree of physical constraints, for example issues of shelving reflecting books as physical objects. In the digital domain there is no shelf, no physical constraint, and indeed with hyperlinks there’s the possibility of expressing a rich level of interconnectedness, or intertwingularity that would not have been impossible with pre-digital semi-manual classification systems. Clay goes on to discuss how tagging hyperlinks creates a “market logic” dynamic classification system that frees the content creator attempting to grapple with descriptive metadata from having to decide which pre-defined category box his/her object fits in.

This richly dynamic informal classification based upon every individual’s unique perspective (and therefore context) is at the same time its greatest strength and its greatest weakness. The impermanence, fluidity and highly contextualized nature of folksonomies can create new links or routes of inquiry for the seeker of information yet it can also shield from view the exact object being searched for as a result of nobody using the same tags that you would have used because their context is different (though of course there’s always the brute force method of resource discovery by just searching largely unclassified data using Google). Where folksonomies gain however is in their scalability. Folksonomies benefit from a kind of wisdom of crowds effect when large numbers of individuals are tagging objects such as links.

What I’ve been increasingly thinking of attempting to implement in our learning content management system is a resource discovery mechanism that uses formal classification for each object as part of its metadata but also allowing learners to tag resources for their own purposes. It would be interesting to see how informal tagging complements formal classification and how learners will use both method to find resources. Could we expect the formal classification to define the nature of an object, by stating its intended use as declared by the teacher for example, but find that the tags describe how objects get used in practice?

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One thought on “There is no shelf”
  1. Hi David, I only just say this post come through in my aggregator, so apologies for the late reply. I think what you are describing is similar to the approach we are taking in our repository project. We do have a high-level subject classification that does have a local grounding; it was developed by our Credit Transfer people to classify which courses transfer to which in B.C. SO it is somewhat local and hopefully at a high level represents generally the areas of study in B.C., the primary source of content for our repository. But within any of these subject areas there is also the specific topic of study; for example, ‘sustainability’ can be a topic within a larger subject area of ‘Resource Based Industries’ but it can also be a topic area within something like ‘Educational Technology.’ For this kind of topic level keywords, we are giving people a totally unconstrained keyword field to enter whatever they like. The plan is to then mine or otherwise ‘garden’ these keywords in some ways, eother letting them aggregate unfettered by any other intervention, or else intervene to cull out recurring ones and see if that can refine the higher level subject taxonomy. Honestly, I have no idea how this will work, but it’s my effort to try and find some middle ground between these approaches. Honestly, I am pretty skeptical of the totally unconstrained folksonomies approach for collections of any size, but that doesn’t mean I’m a big believer in fixed taxonomy approach either. In the end, I work for a provincial government agency, and the willingness of such institutions to not have some fixed method for classifying resources that at the end of the day they paid for seems pretty low.

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