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  How to measure reader performance?  
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Subject - and reader oriented metadata
Library science

Adding metadata to information is an ancient discipline. It was already practiced by the people setting up the library of Alexandria (290 BC). They were also confronted with the question: "how should we groups our books such that all readers can find the book they need".

This science has evolved over the centuries. The best known categorizations today are the Dewey decimal classification and the MARC from the Library of Congress (US).

Physical books and electronic information

Today however, a lot of our information is in electronic form. Most of this information can be accessed through a network (e.g. the internet). This has two major advantages:

  1. Every document is just a mouse-click away, whether it is in Rosmalen or in Australia.
  2. We can make many references to the same document.

The possibility to make several groupings is used by Amazon.com to find the same book both by the author, the title and the subject.

Novices and Experts

Experts understand the subject. Experts know what is relevant and what is less relevant. Experts can choose information based on their understanding. Experts can make a classification of the information because they know what is most relevant and what is less relavant. An expert classification will follow the relevant characteristics of the subject.

Novices know little about the subject. Novices do not know exactly what information they need. Therefore it is hard for novices to search on the relevant characteristics of the subject. Novices only know:

  • their goals
  • their conditions

Therefore we need two classifications:

  1. A classification that follows the relevant characteristics of the subject. This subject-oriented classification is especially suitable for experts.
  2. A classification that is organized around users, goals and conditions. This reader-oriented classification is especially suitable for novices.
Optimal metadata

When information is in electronic form we can make many classifications of the same information. This allows us to put the same information into two classifications: one for experts and one for novices.