Categorisation occurs pretty much everywhere in human life. Most of the time, most of the categorisation appears so obvious that we don’t pay particular attention to it. Every once in a while, though, a case crops up which suddenly calls our assumptions about categorisation into question, and raises uncomfortable questions about whether there’s something fundamentally wrong in how we think about the world.
In this article, I’ll look at one important aspect of categorisation, namely the difference between crisp sets and fuzzy sets. It looks, and is, simple, but it has powerful and far-reaching implications for making sense of the world.
I’ll start with the example of whether or not you own a motorbike. At first glance, this looks like a straightforward question which divides people neatly into two groups, namely those who own motorbikes, and those who don’t. We can represent this visually as two boxes, with a crisp dividing line between them, like this.
However, when you’re dealing with real life, you encounter a surprising number of cases where the answer is unclear. Suppose, for instance, that someone has jointly bought a motorbike with their friend. Does that person count as being the owner of a motorbike, when they’re actually the joint owner? Or what about someone who has bought a motorbike on hire purchase, and has not yet finished the payments?
My usual response to badly assembled questionnaires involves a rant, followed by a dissection of the methodological issues involved and of various relevant bodies of theory.
Sometimes, though, a questionnaire manages to achieve a level of badness so extreme that it transcends its own awfulness.
Today’s example is one of those. It’s a question from an unidentified questionnaire. It’s asking about sexuality. It offers one option which you don’t usually see in this context. Admittedly, it’s probably the result of a copy and paste error, but that’s a minor detail. (Yes, I’m being ironic there…)
To what extent does the language of Shakespeare’s plays indicate a male-dominated world? One way to see is by looking at the distribution of gendered words within the texts.
The figure below shows the location of the words he, him, his, she and her in Midsummer Night’s Dream. Each of the tiny rectangles represents a word in the text; the coloured words represent the keywords, and the blank rectangles represent the other words. This representation ignores linebreaks in the original text. The images are roughly equivalent to a miniaturised image of the text laid out as a scroll, with the keywords marked with coloured highlighter.
In each pair of images below, the nominative forms of the keywords are in red, and the other forms such as accusatives are in green, to show whether one gender appears more often in an active role.
(Apologies to any readers who are red/green colour blind; the Search Visualizer software itself takes account of colour blindness in its options, but shrinking the images down to fit into blog format loses the contrast.)
The column on the left shows the distribution of the words he/him/his in Midsummer Night’s Dream. The column on the right shows the distribution of the words she/her in the same play.
There are more male pronouns, but the difference is not huge; both male and female pronouns occur frequently throughout the play.
Here, for comparison, is the corresponding figure for Romeo and Juliet.
This is the 100th post on the Hyde & Rugg blog. We’re taking this opportunity to look back at what we’ve covered and look forward to what comes next.
The image below shows some of the main themes and outputs so far, in the “knowledge cycle” format that underlies our Verifier framework for tackling human error. If you’ve come to this blog after reading Blind Spot, you might be pleased to discover that we’ve been covering the contents of Verifier here in more depth than was possible in the book, and that we’re well on the way to a full description.
In the image below you can see some of the main themes and topics we’ve covered so far in the “knowledge cycle” format that underlies our Verifier framework for tackling human error. If you’ve come to this blog after reading Blind Spot, it’s worth knowing that we’ve covered some of the the contents of Verifier in more depth here than was possible in the book, and that we’re well on the way to a full description.
The knowledge cycle, and topics that we’ve blogged about