Quick, who’s this guy:
If you answered “Einstein’s less famous brother, Einbert?” you wouldn’t actually be too far from the truth. It’s Noam Chomsky. He’s so famous his name comes pre-installed in Microsoft Word’s spell checker. (Did you mean “chomp sky?”)
If you’ve got a good history or government background, you may be thinking, “Oh yeah, the anarchy guy.” He may be, but his greatest intellectual achievement has nothing to do with anarchy and everything to do with linguistics. That achievement would be generativity.
Gen-er-a-tiv-i-ty. Write it down, it will be on the test.
Generativity was a game-changer for linguistics. Before that point, linguistics was basically phrenology, which I’ve mentioned before. Phrenology is to modern linguistics what naturalism is to modern biology. Phrenologists collected knowledge about languages haphazardly, without a whole lot of underlying theoretical structure. I mean, there was some, (I’ll talk about what the brother’s Grimm did on their weekends off later) but it was pretty confined. And a lot of it, let’s be honest, was about proving that Europe was best. The monumental Oxford English Dictionary is a good example of that mindset. They wanted to collect every single word in English language and pin it neatly to the page with a little series of notes about it and a list of sightings in the wild. It was, and remains, a grand undertaking and a staggering achievement… but modern linguists aren’t collectors anymore.
That’s because the end goal of modern linguistics is to solve language. The field is working to put together a series of rules that will actually describe and predict all human language. Not in the mind reader, fortune teller sense of predict. I mean that, with the right rules, we should be able to generate all possible sentences. In a generative way. By using generativity.
So why is this important?
Lots of reasons! Here, let me list them, because lists are fun to read.
- This turned linguistics from an interesting hobby for rich people into a science. If you have rules, you can make predictions about what those rules will produce and then test those predictions. Testing predictions is also known as science. It’s also something that linguistics as a whole has been a little… hesitant to adopt, but that’s another story.
- Suddenly computers! Computer programming is, at its most basic level, a series of rules. Linguistics is now dedicated to producing a series of rules. Bada-bing, bada-boom, universal translator. (It doesn’t work that way, but, in theory, it eventually can.)
- Now we have a framework that we can use to figure out how to ask questions. We have a goal. Things are organized.
Now for the promised test.
What term is used to describe the current goal of linguistics; i.e. to generate a set of rules that can accurately describe and predict language usage? (Seriously, I’m not going to give you the answer. Just scroll up.)