Not that I want to get into analysis of hard-coding or not in search results but it is an interesting lead into issues a bit closer to home.
To what extent does subject identification have built-in biases that impact user communities?
Or less abstractly, how would we go about discovering and perhaps countering such bias?
For countering the bias you can guess that I would suggest topic maps.
The more pressing question is and one that is relevant to topic map design, is how to discover our own biases?
What seems perfectly natural to me, with a background in law, biblical studies, networking technologies, markup technologies, and now semantic technologies, may seem so to other users.
To make matters worse, how do you ask a user about information they did not find?
- How would you survey users to discover biases in subject identification? (3-5 pages, no citations)
- How would you discover what information users did not find? (3-5 pages, no citations)
- Class project: Design and test a survey for bias in a particular subject identification. (assuming permission from a library)
PS: There are biases in algorithms as well but we will cover those separately.