Ok, this is really just an introductory paragraph, nothing to comment on here except that it sounds like I’m way more ahead than I am (it’s the end of week three of uni and this content is set for week eight), but I haven’t had time off when the mid semester break is scheduled – I’ll do that in the school holidays.
4.2 Locating Resources by Subject
I have written before about the problems with the LCSHs, so I won’t go into it again here.
Perform some subject searches on the Library of Congress catalogue, by choosing ‘Browse’ and using the ‘SUBJECTS beginning with’ option.
So, I began with Hockey – a sport I have recently started playing, and then chose the narrower term “Field hockey” – even field hockey has about 30 sub divisions, although they are really uneven. Note in the below example that there are subheadings for Argentina (who, incidentally, I went to see play against Australia on Saturday) but none for Australia.
Then I looked at the SCIS subject headings. The first, noticeable difference is that hockey is “used for” field hockey, and a narrower term is ice hockey (whereas I am pretty sure the LCSH use hockey to mean ice hockey by default). Most students in Australia looking for ice hockey resources would search “ice hockey”, so this difference in vocabulary is important.
Next I searched the Thesaurus for Graphic Materials for “Emus”
Then I looked at items in the LoC that had this description – to find there was only one.
With regards to classification systems, there are some I have read about that were not mentioned in the textbook. Sweden used to have their own classification system, but it started to be phased out in 2008. There was also recent news coverage about Galiwin’ku Library that abandoned the DDC in favour for a locally developed system that works for the local population. You can hear more about it on the Turbitt n Duck podcast.
A lot of services use social tagging – Flickr and YouTube in particular – and this is vital when the resource at hand can’t be searched for text because it is a visual medium. However, the risks of inaccurate tags or multiple tags meaning the same thing are high. For example, on Friday last week it was the day of the strike of school students to protest political inaction on climate change. I noticed on Twitter that quite a few hashtags were trending including #SchoolStrike #StrikeForClimate #strike4climate #ClimateStrike and so on. Now as a human being, I can see that these hashtags are all about the same thing, but Twitter’s algorithms can’t.