aryanhwy: (widget)
This morning I received an email from the University media office asking if I would be free about 9:50 to be interviewed on BBC Radio Newcastle in my capacity as an onomastic expert. Of course, though the email was sent around 8:50, I didn't read it until about 9:40! Thankfully, I got a very prompt reply to my reply to it saying that the interview was planned for 10:10, not 9:50. I got a few calls to test that my number worked, was told that the piece was in response to this article in the Independent so I could do a quick bit of research and sketch out some thoughts, and then a few minutes before 10:10 my phone rang, I briefly spoke to the presenter to confirm how my name was pronounced while a song was playing on the radio, and then *poof* there I was, on air, airing my opinions about names.

It was pretty awesome.
aryanhwy: (Default)
The phenomenon that I noted some weeks back, namely that in many datasets (having roughly equal numbers of boy's names and girl's names) sorted alphabetically it's possible to draw a line such that the majority of the women's names are on one side and the majority of men's on the other, has evidence in contemporary naming practices too. Joel just pointed me towards this article, and while they do say some moderately silly things (such as "Girls' names are more diverse than ever" -- not really. The feminine name pool has always been more diverse than the masculine. It could very well be that girls' names are more diverse than they have been in the 20th C, but that's not the same as "ever"), but, on the basis of last year's social security baby name data, they comment:
Similarly, the most popular first letter for boys; names is J, as it was for much of the 20th century. The most popular first letter for girls is the once-obscure A, thanks partly to rising names like Avery, Arianna and Ava.

Which is in line with the phenomenon that I've noticed.


Jun. 6th, 2011 09:36 pm
aryanhwy: (Default)
I do a lot of data-crunching with names. Names from all sorts of times and places. And I have a pattern for how I crunch them: After I've transcribed a data set, I sort it (either the entire data set, or year at a time) alphabetically. Then I get all examples of the same name together, makes it easier to count them, and I also get variants grouped close enough together that I can often spot variant pairs that I wouldn't otherwise have recognized if they were not near each other.

You know what I've noticed? There is a strong alphabetic distinction between girl's names and boy's names. Girl's names are more likely to start with letters in the beginning of the alphabet, and boy's with ones at the end of the alphabet. This is particularly clear to see in 16th C English datasets. To give you an example, the following are the names of people married in Chedzoy, Somerset, in 1560:

Girls: Agnes, Alice, Eliz. (2), Johan, Margaret, Margerye
Boys: John (2), Richard, Thomas (2), William (2)

Except for the exception of John, I could draw a line partway through the list and all the names on one side are girl's and all the ones on the other side are boy's.



aryanhwy: (Default)

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