Librarians are often responsible for determining controlled vocabularies and taxonomies, which are basically fancy words for index terms. So, if you want to reference a beer, you could do so through a variety of terms or classifications, including:

1. Ingredients

2. Texture

3. Taste

4. Type of beverage (alcoholic, etc…)

5. Cost

6. Location

And so on and so on.

These controlled vocabularies have been the basis of libraries and any sort of organizational system for years and years, remaining a relatively static concept for quite some time. With the advent of Web 2.0 (think wikis, delicious, flickr, blogs, RSS feeds, and the lot), we have a public demand for user participation in these schemes. Basically, people are tagging items in any way they see fit, librarians and their stodgy controlled vocabularies be damned. Delicious or flickr or two examples, very popular ones at that. So, everyone applies a tag to an object or website and the most common ones will appear if enough people participate; those most common ones are the best classification terms because they are the most popular and most reflect the public’s understanding of the object

All that serves as an elaborate introduction to the following game/experiment. If you think it is easy to come up with classification terms, try this game. The whole purpose is to see an image and come up with words to describe it. Easy enough. The trick is that you have to agree with a person you are playing with, a person who never directly communciate with. It becomes very fascinating (and frustrating) to see which terms are identified with which images. Brought to you by the good people at Carnegie Mellon. Enjoy.

By Michael Gallagher

My name is Michael Sean Gallagher. I am a Lecturer in Digital Education at the Centre for Research in Digital Education at the University of Edinburgh. I am Co-Founder and Director of Panoply Digital, a consultancy dedicated to ICT and mobile for development (M4D); we have worked with USAID, GSMA, UN Habitat, Cambridge University and more on education and development projects. I was a researcher on the Near Futures Teaching project, a project that explores how teaching at The University of Edinburgh unfold over the coming decades, as technology, social trends, patterns of mobility, new methods and new media continue to shift what it means to be at university. Previously, I was the Research Associate on the NERC, ESRC, and AHRC Global Challenges Research Fund sponsored GCRF Research for Emergency Aftershock Forecasting (REAR) project. I was an Assistant Professor at Hankuk University of Foreign Studies (한국외국어대학교) in Seoul, Korea. I have also completed a doctorate at University College London (formerly the independent Institute of Education, University of London) on mobile learning in the humanities in Korea.

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