I’ve been doing some product development consulting for a content-centric company, and started to wonder if ethnoclassification could benefit them somehow. I was hard-pressed to think of how, after the content was tagged, the tags would lead to better sets of content. Although they have a lot of content, they only have a few authors who do the classification. When I returned to what others have written on the topic, I think the idea of ethnoclassification was so enthralling we didn’t look as closely at how it facilitates retrieval and the relationship to the content set and authoring process.
It seems that selecting a classification technique should factor in how much control we want over retrieval, how much content there is, and how many authors there are. Ethnoclassification becomes more useful when there’s a ton of authors and you can start to see relationships emerge among tags.
- Large content set, many authors, low control over retrieval: Ethnoclassification makes sense when seredipitous retrieval is desired and a critical mass of authors are generating tags, like on Flickr
- Large content set, many authors, high control over retrieval: A robust structure is needed to ensure end users can find information, like the ontology-driven Cisco.com
- Medium content set, medium authors, medium control over retrieval: This situation allows for interesting hybrids, like the Pratt Talent site that balances ethnoclassification with a controlled vocabulary
- Medium content set, medium authors, high control over retrieval: traditional controlled vocabularies/taxonomies make sense, and scale down to the point where there is so little content/few authors that no classification is needed