Are “a female” and “a male” used differently?

In this first part of this two-post series, I looked at how “a male” and “a female” were used on Twitter. I found that one part of speech tagger tagged “male” as a proper noun really frequently (which is weird, cause it isn’t one) and that overall the phrase “a female” was waaaay more frequent. Which is  interesting in itself, since my initial question was “are these terms used differently?” and these findings suggest that they are. But the second question is how are these terms used differently? To answer this, we’ll need to get a little more qualitative with it.

Using the same set of tweets that I collected last time, I randomly selected 100 tweets each from the “a male” and “a female” dataset. Then I hand tagged each subset of tweets for two things: the topic of the tweet (who or what was being referred to as “male” or “female”) and the part of speech of “male”  or “female”.

Who or what is being called “male” or “female”?

Rplot

Because there were so few tweets to analyze, I could do a content analysis. This is a methodology that is really useful when you don’t know for sure ahead of time what types of categories you’re going to see in your data. It’s like clustering that a human does.

Going into this analysis, I thought that there might be a difference between these datasets in terms of how often each term was used to refer to an animal, so I tagged tweets for that. But as I went through the tweets, I was floored by the really high number of tweets talking about trans people, especially Mack Beggs, a trans man from Texas who was forced to wrestle in the women’s division. Trans men were referred to as “a male” really, really often. While there wasn’t a reliable difference between how often “a female” and “a male” was used to refer to animals or humans, there was a huge difference in terms of how often they were  used to refer to trans people. “A male” was significantly more likely to be used to describe a trans person than “a female” (X2 (2, N = 200) = 55.33, p <.001.)

Part of Speech

Since the part of speech taggers I used for the first half of my analysis gave me really mixed results, I also hand tagged the part of speech of “male” or “female” in my samples. In line with my predictions during data collection, the only parts of speech I saw were nouns and adjectives.

When I looked at just the difference between nouns and adjectives, there was a little difference, but nothing dramatic. Then, I decided to break it down a little further. Rather than just looking at the differences in part of speech between “male” and “female”, I looked at the differences in part of speech and whether the tweet was about a trans person or a cis (not trans) person.

Rplot01

For tweets with “female”, it was used as a noun and an adjective at pretty much the same rates regardless of whether someone was talking about a trans person or a cis (non-trans) person. For tweets with “male”, though, when the tweet was about a trans person, it was used almost exclusively as a noun.

And there was a huge difference there. A large majority of tweets with “a male” and talking about a trans person used “male” as a noun. In fact, more than a third of my subsample of tweets using “a male” were using it as a noun to talk about someone who was trans.

So what’s going on here? This construction (using “male” or “female” as a noun to refer to a human) is used more often to talk about:

  1. Women. (Remember that in the first blog post looking at this, I found that “a female” is twice a common as “a male.)
  2. Trans men.

These both make sense if you consider the cultural tendency to think about cis men as, in some sense, the “default”. (Deborah Tannen has a really good discussion of this her article “Marked Women, Unmarked Men“. “Marked” is a linguistics term which gets used in a lot of ways, but generally means something like “not the default” or “the weird one”.) So people seem to be more likely to talk about a person being “a male” or “a female” when they’re talking about anyone but a cis man.

A note on African American English

giphy.gif

I should note that many of the tweets in my sample were African American English, which is not surprising given the large Black community on Twitter, and that use of “female” as a noun is a feature of this variety.  However, the parallel term used to refer to men in this variety is not “a man” or even “a male”, but rather “nigga”, with that spelling. This is similar to “dude” or “guy”: a nonspecific term for any man, regardless of race, as discussed at length by Rachel Jeantal here. You can see an example of this usage in speech above (as seen in the Netflix show “The Unbreakable Kimmy Schmidt“) or in this vine. (I will note, however, that it only has this connotation if used by a speaker of African American English. Borrowing it into another variety, especially if the speaker is white, will change the meaning.)

Now, I’m not a native user of African American English, so I don’t have strong intuitions about the connotation of this usage. Taylor Amari Little (who you may know from her TEDx talk on Revolutionary Self-Produced Justice) is, though, and tweeted this (quoted with permission):

If they call women “females” 24/7, leave em alone chile, run away

And this does square with my own intuitions: there’s something slightly sinister about someone who refers to women exclusively as “females”. As journalist Vonny Moyes pointed out in her recent coverage of ads offering women free rent in exchange for sexual favors, they almost refer to women as “girls or females – rarely ever women“. Personally, I find that very good motivation not to use “a male” or “a female” to talk about any human.

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Preference for wake words varies by user gender

I recently read a very interesting article on the design of aspects of choosing a wake word, the word you use to turn on a voice-activated system. In Star Trek it’s “Computer”, but these days two of the more popular ones are “Alexa” and “OK Google”. The article’s author was a designer and noted that she found “Ok Google” or “Hey Google” to be more pleasant to use than “Alexa”. As I was reading the comments (I know, I know) I noticed that a lot of the people who strongly protested that they preferred “Alexa” had usernames or avatars that I would associate with male users. It struck me that there might be an underlying social pattern here.

So, being the type of nerd I am, I whipped up a quick little survey to look at the interaction between user gender and their preference for wake words. The survey only had two questions:

  • What is your gender?
    • Male
    • Female
    • Other
  • If Google Home and the Echo offered identical performance in all ways except for the wake word (the word or phrase you use to wake the device and begin talking to it), which wake word would you prefer?
    • “Ok Google” or “Hey Google”
    • “Alexa”

I included only those options becuase those are the defaults–I am aware you can choose to change the Echo’s wake word. (And probably should, given recent events.) 67 people responded to my survey. (If you were one of them, thanks!)

So what were the results? They were actually pretty strongly in line with my initial observations: as a group, only men preferred “Alexa” to “Ok Google”. Furthermore, this preference was far weaker than people of other genders’ for “Ok Google”. Women preferred “Ok Google” at a rate of almost two-to-one, and no people of other genders preferred “Alexa”.

I did have a bit of a skewed sample, with more women than men and people of other genders, but the differences between genders were robust enough to be statistically significant (c2(2, N = 67) = 7.25, p = 0.02)).

genderandwakewords

Women preferred “Ok Google” to “Alexa” 27:11, men preferred “Alexa” to “Ok Google” 14:11, and the four people of other genders in my survey all preferred “Ok Google”.

So what’s the take-away? Well, for one, Johna Paolino (the author of the original article) is by no means alone in her preference for a non-gendered wake word. More broadly, I think that, like the Clippy debacle, this is excellent evidence that there are strong gendered differences in how users’ gender affects their interaction with virtual agents. If you’re working to create virtual agents, it’s important to consider all types of users or you might end up creating something that rubs more than half of your potential customers the wrong way.

My code and data are available here.

What’s the difference between & and +?

So if you’re like me, you sometimes take notes on the computer and end up using some shortcuts so you can keep up with the speed of whoever’s talking. One of the short cuts I use a lot is replacing the word “and” with punctuation. When I’m handwriting things I only ever use “+” (becuase I can’t reliably write an ampersand), but in typing I use both “+” and “&”. And I realized recently, after going back to change which one I used, that I had the intuition that they should be used for different things.

Ampersand-handwriting-3.png

I don’t use Ampersands when I’m handwriting things becuase they’re hard to write.

Like sometimes happens with linguistic intuitions, though, I didn’t really have a solid idea of how they were different, just that they were. Fortunately, I had a ready-made way to figure it out. Since I use both symbols on Twitter quite a bit, all I had to do was grab tweets of mine that used either + or & and figure out what the difference was.

I got 450 tweets from between October 7th and November 11th of this year from my own account (@rctatman). I used either & or + in 83 of them, or roughly 18%. This number is a little bit inflated because I was livetweeting a lot of conference talks in that time period, and if a talk has two authors I start every livetweet from that talk with “AuthorName1 & AuthorName2:”. 43 tweets use & in this way. If we get rid of those, only around 8% of my tweets contain either + or &. They’re still a lot more common in my tweets than in writing in other genres, though, so it’s still a good amount of data.

So what do I use + for? See for yourself! Below are all the things I conjoined with + in my Twitter dataset. (Spelling errors intact. I’m dyslexic, so if I don’t carefully edit text—and even sometimes when I do, to my eternal chagrin—I tend to have a lot of spelling errors. Also, a lot of these tweets are from EMNLP so there’s quite a bit of jargon.)

  • time + space
  • confusable Iberian language + English
  • Data + code
  • easy + nice
  • entity linking + entity clustering
  • group + individual
  • handy-dandy worksheet + tips
  • Jim + Brenda, Finn + Jake
  • Language + action
  • linguistic rules + statio-temporal clustering
  • poster + long paper
  • Ratings + text
  • static + default methods
  • syntax thing + cattle
  • the cooperative principle + Gricean maxims
  • Title + first author
  • to simplify manipulation + preserve struture

If you’ve had some syntactic training, it might jump out to you that most of these things have the same syntactic structure: they’re noun phrases! There are just a couple of exception. The first is “static + default methods”, where the things that are being conjoined are actually adjectives modifying a single noun. The other is “to simplify manipulation + preserve struture”. I’m going to remain agnostic about where in the verb phrase that coordination is taking place, though, so I don’t get into any syntax arguments ;). That said, this is a fairly robust pattern! Remember that I haven’t been taught any rules about what I “should” do, so this is just an emergent pattern.

Ok, so what about &? Like I said, my number one use is for conjunction of names. This probably comes from my academic writing training. Most of the papers I read that use author names for in-line citations use an & between them. But I do also use it in the main body of tweets. My use of & is a little bit harder to characterize, so I’m going to go through and tell you about each type of thing.

First, I use it to conjoin user names with the @ tag. This makes sense, since I have a strong tendency to use & with names:

  • @uwengineering & @uwnlp
  • @amazon @baidu @Grammarly & @google

In some cases, I do use it in the same way as I do +, for conjoining noun phrases:

  • Q&A
  • the entities & relations
  • these features & our corpus
  • LSTM & attention models
  • apples & concrete
  • context & content

But I also use it for comparatives:

  • Better suited for weak (bag-level) labels & interpretable and flexible
  • easier & faster

And, perhaps more interestingly, for really high-level conjugation, like at the level of the sentence or entire verb phrase (again, I’m not going to make ANY claims about what happens in and around verbs—you’ll need to talk to a syntactician for that!).

  • Classified as + or – & then compared to polls
  • in 30% of games the group performance was below average & in 17% group was worse than worst individual
  • math word problems are boring & kids learn better if they’re interested in the theme of the problem
  • our system is the first temporal tagger designed for social media data & it doesn’t require hand tagging
  • use a small labeled corpus w/ small lexicon & choose words with high prob. of 1 label

And, finally, it gets used in sort of miscellaneous places, like hashtags and between URLs.

So & gets used in a lot more places than + does. I think that this is probably because, on some subconscious level I consider & to be the default (or, in linguistics terms, “unmarked“). This might be related to how I’m processing these symbols when I read them. I’m one of those people who hears an internal voice when reading/writing, so I tend to have canonical vocalizations of most typed symbols. I read @ as “at”, for example, and emoticons as a prosodic beat with some sort of emotive sound. Like I read the snorting emoji as the sound of someone snorting. For & and +, I read & as “and” and + as “plus”. I also use “plus” as a conjunction fairly often in speech, as do many of my friends, so it’s possible that it may pattern with my use in speech (I don’t have any data for that, though!). But I don’t say “plus” nearly as often as I say “and”. “And” is definitely the default and I guess that, by extension, & is as well.

Another thing that might possibly be at play here is ease of entering these symbols. While I’m on my phone they’re pretty much equally easy to type, on a full keyboard + is slightly easier, since I don’t have to reach as far from the shift key. But if that were the only factor my default would be +, so I’m fairly comfortable claiming that the fact that I use & for more types of conjunction is based on the influence of speech.

A BIG caveat before I wrap up—this is a bespoke analysis. It may hold for me, but I don’t claim that it’s the norm of any of my language communities. I’d need a lot more data for that! That said, I think it’s really neat that I’ve unconsciously fallen into a really regular pattern of use for two punctuation symbols that are basically interchangeable. It’s a great little example of the human tendency to unconsciously tidy up language.

Can a computer write my blog posts?

This post is pretty special: it’s the 100th post I’ve made since starting my blog! It’s hard to believe I’ve been doing this so long. I started blogging in 2012, in my final year of undergrad, and now I’m heading into my last year of my PhD. Crazy how fast time flies.

Ok, back on topic. As I was looking back over everything I’ve written, it struck me that 99 posts worth of text on a very specific subject domain (linguistics) in a very specific register (informal) should be enough text to train a simple text generator.

So how did I go about building a blog bot? It was pretty easy! All I needed was:

  • 67,000 words of text (all blog posts before this one)
  • 1 R script to tidy up the text
  • 1 Python script to train a Markov Chain  text generator

A Markov Whatnow?

A Markov Chain is a type of simple (but surprisingly powerful) statistical model that tells you, given the item you’re currently on, what item you’re likely to see next. Today we’re going to apply it to whole words in a text.

How does it work? Basically, for each word in your text, you count how many different words occur after it, how many time each shows up and figure out the probability of each transition. So if your text is “The dog ate the apple.”, then there’s a 50% chance that “the” will be followed by “apple”, but a 100% chance that “apple” will be followed by “.”. You can then use these probabilities to generate new sentences, like so:

  • Input: The dog ate the apple.
  • Possible outputs:
    • The apple.
    • The dog ate the apple.
    • The dog ate the dog ate the apple.
    • The dog ate the dog ate the dog ate the dog ate the dog ate the dog ate the dog ate the dog ate the dog ate the dog ate the dog ate the apple. (This may seem silly, but remember that we’re only looking at two words at a time. This model doesn’t “remember” what came earlier or “know” that it’s repeating itself.)

OK, now for the actual training of the model. If you want to play around with this yourself, all my code and text is up on GitHub here.

  1. First, grab your text. I downloaded all my blog posts as an XML file and used a quick little R script to snag just the text I was interested in.
  2. Next, train your model. I used PyMarkovChain, but you can always write your own if you’re feeling it.
  3. Finally, use the model to generate text.

Alright, now I’ve got (admittedly very, very dumb) little program that kinda knows what I write like when I’m blogging. How well does it do at pretending to be me?

First try:

I’m going to break eye contact, look down at your own personalized ASR system

Well that’s… vaguely disturbing. But also not really very human-sounding, and certainly not something I’d write in a blog post. Let’s give it a couple more tries and see if it does any better.

  • But, if frosting has to have a career where you learned it from Clarice
  • We only perceive the things that our bodies and sensory organs and brains can perceive, and we really don’t care about to tell the sounds in the second part, I was expecting to see whether something is grammatical is to put up a bunch of historical stuff that we’ve all heard about the structure in the arts and sciences were depressed
  • (Which is really interesting–I’d love to learn your language doesn’t make you very, very good a recognizing speech
  • Believe me, I know what you can uncontract them and what’s the take-away
    People with short face syndrome–yes, that’s a classical description of what a language does, along with parallels drawn to another, related, languages
  • Short answer: they’re all correct
  • And those speakers are aware of
  • The Job Market for Linguistics PhDsWhat do you much

Hmm, not so great. The only output that sounded good to me was “Short answer: they’re all correct”. And that was just the exact same phrasing I used in this blog post. No wonder it sounds just like me; it is me!

So it looks like I won’t be handing the reins over to Making Noise and Hearing Things bot any time soon. True, I could have used a fancier tool, like a Recurrent Neural Network. But to be perfectly honest, I have yet to see any text generation system that can produce anything even close to approximating a human-written blog post. Will we get there? Maybe. But natural language generation, especially outside of really formulaic things like weather or sports reporting, is a super hard problem. Heck, we still haven’t gotten to point where computers can reliably solve third-grade math word problems.

The very complexities that make language so useful (and interesting to study) also make it so hard to model. Which is good news for me! It means there’s still plenty of work to do in language modelling and blogging.

Google’s speech recognition has a gender bias

In my last post, I looked at how Google’s automatic speech recognition worked with different dialects. To get this data, I hand-checked annotations  more than 1500 words from fifty different accent tag videos .

Now, because I’m a sociolinguist and I know that it’s important to stratify your samples, I made sure I had an equal number of male and female speakers for each dialect. And when I compared performance on male and female talkers, I found something deeply disturbing: YouTube’s auto captions consistently performed better on male voices than female voice (t(47) = -2.7, p < 0.01.) . (You can see my data and analysis here.)

accuarcyByGender

On average, for each female speaker less than half (47%) her words were captioned correctly. The average male speaker, on the other hand, was captioned correctly 60% of the time.

It’s not that there’s a consistent but small effect size, either, 13% is a pretty big effect. The Cohen’s d was 0.7 which means, in non-math-speak, that if you pick a random man and random woman from my sample, there’s an almost 70% chance the transcriptions will be more accurate for the man. That’s pretty striking.

What it is not, unfortunately, is shocking. There’s a long history of speech recognition technology performing better for men than women:

This is a real problem with real impacts on people’s lives. Sure, a few incorrect Youtube captions aren’t a matter of life and death. But some of these applications have a lot higher stakes. Take the medical dictation software study. The fact that men enjoy better performance than women with these technologies means that it’s harder for women to do their jobs. Even if it only takes a second to correct an error, those seconds add up over the days and weeks to a major time sink, time your male colleagues aren’t wasting messing with technology. And that’s not even touching on the safety implications of voice recognition in cars.

 

So where is this imbalance coming from? First, let me make one thing clear: the problem is not with how women talk. The suggestion that, for example, “women could be taught to speak louder, and direct their voices towards the microphone” is ridiculous. In fact, women use speech strategies that should make it easier for voice recognition technology to work on women’s voices.  Women tend to be more intelligible (for people without high-frequency hearing loss), and to talk slightly more slowly. In general, women also favor more standard forms and make less use of stigmatized variants. Women’s vowels, in particular, lend themselves to classification: women produce longer vowels which are more distinct from each other than men’s are. (Edit 7/28/2016: I have since found two papers by Sharon Goldwater, Dan Jurafsky and Christopher D. Manning where they found better performance for women than men–due to the above factors and different rates of filler words like “um” and “uh”.) One thing that may be making a difference is that women also tend not to be as loud, partly as a function of just being smaller, and cepstrals (the fancy math thing what’s under the hood of most automatic voice recognition) are sensitive to differences in intensity. This all doesn’t mean that women’s voices are more difficult; I’ve trained classifiers on speech data from women and they worked just fine, thank you very much. What it does mean is that women’s voices are different from men’s voices, though, so a system designed around men’s voices just won’t work as well for women’s.

Which leads right into where I think this bias is coming from: unbalanced training sets. Like car crash dummies, voice recognition systems were designed for (and largely by) men. Over two thirds of the authors in the  Association for Computational Linguistics Anthology Network are male, for example. Which is not to say that there aren’t truly excellent female researchers working in speech technology (Mari Ostendorf and Gina-Anne Levow here at the UW and Karen Livescu at TTI-Chicago spring immediately to mind) but they’re outnumbered. And that unbalance seems to extend to the training sets, the annotated speech that’s used to teach automatic speech recognition systems what things should sound like. Voxforge, for example, is a popular open source speech dataset that “suffers from major gender and per speaker duration imbalances.” I had to get that info from another paper, since Voxforge doesn’t have speaker demographics available on their website. And it’s not the only popular corpus that doesn’t include speaker demographics: neither does the AMI meeting corpus, nor the Numbers corpus.  And when I could find the numbers, they weren’t balanced for gender. TIMIT, which is the single most popular speech corpus in the Linguistic Data Consortium, is just over 69% male. I don’t know what speech database the Google speech recognizer is trained on, but based on the speech recognition rates by gender I’m willing to bet that it’s not balanced for gender either.

Why does this matter? It matters because there are systematic differences between men’s and women’s speech. (I’m not going to touch on the speech of other genders here, since that’s a very young research area. If you’re interested, the Journal of Language and Sexuality is a good jumping-off point.) And machine learning works by making computers really good at dealing with things they’ve already seen a lot of. If they get a lot of speech from men, they’ll be really good at identifying speech from men. If they don’t get a lot of speech from women, they won’t be that good at identifying speech from women. And it looks like that’s the case. Based on my data from fifty different speakers, Google’s speech recognition (which, if you remember, is probably the best-performing proprietary automatic speech recognition system on the market) just doesn’t work as well for women as it does for men.

The problem with the grammar police

I’ll admit it: I used to be a die-hard grammar corrector. I practically stalked around conversations with a red pen, ready to jump out and shout “gotcha!” if someone ended a sentence with a preposition or split an infinitive or said “irregardless”. But I’ve done a lot of learning and growing since then and, looking back, I’m kind of ashamed. The truth is, when I used to correct people’s grammar, I wasn’t trying to help them. I was trying to make myself look like a language authority, but in doing so I was actually hurting people. Ironically, I only realized this after years of specialized training to become an actual authority on language.

Chicago police officer on segway

I’ll let you go with a warning this time, but if I catch you using “less” for “fewer” again, I’ll have to give you a ticket.

But what do I mean when I say I was hurting people? Well, like some other types of policing, the grammar police don’t target everyone equally. For example, there has been a lot of criticism of Rihanna’s language use in her new single “Work” being thrown around recently. But that fact is that her language is perfectly fine. She’s just using Jamaican Patois, which most American English speakers aren’t familiar with. People claiming that the language use in “Work” is wrong is sort of similar to American English speakers complaining that Nederhop group ChildsPlay’s language use is wrong. It’s not wrong at all, it’s just different.

And there’s the problem. The fact is that grammar policing isn’t targeting speech errors, it’s targeting differences that are, for many people, perfectly fine. And, overwhelmingly, the people who make “errors” are marginalized in other ways. Here are some examples to show you what I mean:

  • Misusing “ironic”: A lot of the lists of “common grammar errors” you see will include a lot of words where the “correct” use is actually less common then other ways the word is used. Take “ironic”. In general use it can mean surprising or remarkable. If you’re a literary theorist, however, irony has a specific technical meaning–and if you’re not a literary theorist you’re going to need to take a course on it to really get what irony’s about. The only people, then, who are going to use this word “correctly” will be those who are highly educated. And, let’s be real, you know what someone means when they say ironic and isn’t that the point?
  • Overusing words like “just”: This error is apparently so egregious that there’s an e-mail plug-in, targeted mainly at women, to help avoid it. However, as other linguists have pointed out, not only is there limited evidence that women say “just” more than men, but even if there were a difference why would the assumption be that women were overusing “just”? Couldn’t it be that men aren’t using it enough?
  • Double negatives: Also called negative concord, this “error” happens when multiple negatives are used in a sentence, as in, “There isn’t nothing wrong with my language.” This particular construction is perfectly natural and correct in a lot of dialects of American English, including African American English and Southern English, not to mention the standard in some other languages, including French.

In each of these cases, the “error” in question is one that’s produced more by certain groups of people. And those groups of people–less educated individuals, women, African Americans–face disadvantages in other aspects of their life too. This isn’t a mistake or coincidence. When we talk about certain ways of talking, we’re talking about certain types of people. And almost always we’re talking about people who already have the deck stacked against them.

Think about this: why don’t American English speakers point out whenever the Queen of England says things differently? For instance, she often fails to produce the “r” sound in words like “father”, which is definitely not standardized American English. But we don’t talk about how the Queen is “talking lazy” or “dropping letters” like we do about, for instance,  “th” being produced as “d” in African American English. They’re both perfectly regular, logical language varieties that differ from standardized American English…but only one group gets flack for it.

Now I’m not arguing that language errors don’t exist, since they clearly do. If you’ve ever accidentally said a spoonerism or suffered from a tip of the tongue moment then you know what it feel like when your language system breaks down for a second. But here’s a fundamental truth of linguistics: barring a condition like aphasia, a native speaker of a language uses their language correctly. And I think it’s important for us all to examine exactly why it is that we’ve been led to believe otherwise…and who it is that we’re being told is wrong.

 

How to make STEM classrooms more inclusive

This post is a bit of a departure from my usual content. I’m assuming two things about you, the reader:

  1. You teach/learn in a STEM classroom
  2. You’d like to be more inclusive

If that’s not you, you might want to skip this one. Sorry; I’ll be back to my usual haunts with the next post.

If you’re still with me, you may be wondering what triggered this sudden departure from fun facts about linguistics. The answer is that I recently had an upsetting experience, and it’s been been niggling at me. I’m a member of an online data analysis community that’s geared towards people who program professionally. Generally, it’s very helpful and a great way to find out about new packages and tricks I can apply in my work. The other day, though, someone posted a link to a project designed to sort women by thier physical attractiveness. I commented that it was not really appropriate for a professional environment, and was especially off-putting to the women in the group. I’m not upset that I spoke out, but I’m a little unhappy that I had to. I’m also upset that at least one person thought my criticisms were completely unnecessary. (And, yes, both the person who originally posted the link and the aforementioned commenter are male.)

It got me thinking about inclusiveness in professional spaces, though. Am I really doing all I can to ensure that the field of linguistics is being inclusive? While linguistics as a whole is not horribly skewed male, professional linguists are more likely to be male, especially in computational linguistics. And we are definitely lacking in racial diversity; as the Linguistics Society of America (our main professional organization) puts it:

The population of ethnic minorities with advanced degrees in linguistics is so low in the U.S. that none of the federal agencies report data for these groups.”

If you’re like me, you see that as a huge problem and you want to know what you can do to help fix it. That’s why I’ve put together this list of concrete strategies you can use in your classroom and interactions with students to be more inclusive, especially towards women. (Since I’m not disabled or a member of an ethnic minority group or I can’t speak to those experiences, but I invite anyone who can and has additional suggestions to either comment below or contact me anonymously.) The suggestions below are drawn from my experience as both a teacher and a student, as well as input from the participants and other facilitators in last year’s Including All Students: Teaching in the Diverse Classroom workshops.

For Teachers: 

  • If someone calls you on non-inclusive behavior, acknowledge it, apologize and don’t do it again. I know this seems like an obvious one, but it can be really, really important. For example, a lot of linguistics teaching materials are really geared towards native English speakers. The first quarter I taught I used a problem set in class that required native knowledge of English. When a student (one of several non-native speakers) mentioned it, I was mortified and tempted to just ignore the problem. If I had, though, that student would have felt even more alienated. If someone has the courage to tell you about a problem with your teaching you should acknowledge that, admit your wrong-doing and then make sure it doesn’t happen again.
  • Have space for anonymous feedback. That said, it takes a lot of courage to confront an authority figure–especially if you’re already feeling uncomfortable or like you’re not wanted or valued. To combat that, I give my students a way to contact me anonymously (usually through a webform of some kind). While it may seem risky, all the anonymous feedback I have ever received has been relevant and useful.
  • Group work. This may seem like an odd thing to have on the list, but I’ve found that group work in the classroom is really valuable, both as an instructor and as a student. I may not feel comfortable speaking up or asking question in front of the class as a whole, but small groups are much less scary. My favorite strategy for group work is to put up a problem or discussion question and then drift from group to group, asking students for thier thoughts and answering questions.
  • Structure interactive portions of the class. Sometimes small group work doesn’t work well for your material. It’s still really helpful to provide a structure for students to interact and ask questions, because it lets you ensure that all students are included (it has the additional benefit of keeping everyone awake during those drowsy after-lunch classes). Talbot Taylor, for example, would methodically go around in the classroom in order and ask every single student a question during class. Or you could have every student write a question about the course content to give to you at the end of class that you address at the beginning of the next class. Or, if you have readings, you can assign one or two students to lead the discussion for each reading.
  • Don’t tokenize. This is something that one of the workshop participants brought up and I realized that it’s totally something I’ve been guilty of doing (especially if I know one of my students speaks a rare language). If there is only one student of a certain group in your class, don’t ask them to speak for or represent thier group. So if you have one African American student, don’t turn to them every time you discuss AAE. If they volunteer to speak about, great! But it’s not fair to expect them too, and it can make students feel uncomfortable.
  • If someone asks you to speak to someone else for them, don’t mention the person who asked you. I know this one is oddly specific, but it’s another thing that came out of the workshop. One student had asked thier advisor to ask another faculty member to stop telling sexist jokes in class. Their advisor did so, but also mentioned that it was the student who’d complained, and the second faculty member then ridiculed the student during the next class. (This wasn’t in linguistics, but still–yikes!) If someone’s asking you to pass something on for them, there’s probably a very good reason why they’re not confronting that person directly.
  • Don’t objectify minority students. This one mainly applies to women. Don’t treat women, or women’s bodies, like things. That’s what was so upsetting for me about the machine learning example I brought up at the beginning of the article: the author was literally treating women like objects. Another example comes from geoscience, where a student  tells about their experience at a conference where “lecturers… included… photo[s] of a woman in revealing clothing…. I got the feeling that female bodies were shown not only to illustrate a point, but also because they were thought to be pretty to look at” (Women in the Geosciences: Practical, Positive Practices Toward Parity, Holes et al., P.4).

For Everybody: 

  • Actively advocate for minority students. If you’re outside of a minority that you notice is not receiving equal treatment, please speak up about it. For example, if you’re a man and you notice that all the example sentences in a class are about John–a common problem–suggest a sentence with Mei-Ling, or another female name, instead. It’s not fair to ask students who are being discriminated against to be the sole advocates for themselves. We should all be on the lookout for sneaky prejudices.  
  • Don’t speak for/over minority students. That said, don’t put words in people’s mouths. If you’re speaking up about something, don’t say something like, “I think x is making Sanelle uncomfortable”. It may very well be making Sanelle uncomfortable, but that’s up for Sanelle to say. Try something like “I’m not sure that’s an appropriate example”, instead.

Those are some of my pointers. What other strategies do you have to help make the classroom more inclusive?

What’s the best way to block the sound of a voice?

Atif asked:

My neighbor talks loudly on the phone and I can’t sleep. What is the best method to block his voice noise?

Great question Atif! There are few things more distracting than hearing someone else’s conversation, and only hearing one side of a phone conversation is even worse. Even if you don’t want it to, your brain is trying to fill in the gaps and that can definitely keep you awake. So what’s the best way to avoid hearing your neighbor? Well, probably the very best way is to try talking to them. Failing that, though, you have three main options: isolation, damping and masking.

Ruído Noise 041113GFDL
So what’s the difference between them and what’s the best option for you? Before we get down to the nitty gritty I think it’s worth a quick reminder of what sound actually is: sound waves are just that–waves. Just like waves in a lake or ocean. Imagine you and a neighbor share a small pond and you like to go swimming every morning. Your neighbor, on the other hand, has a motorboat that they drive around on thier side. The waves the motorboat makes keep hitting you as you try to swim and you want to avoid them.  This is very similar to your situation: your neighbor’s voice is making waves and you want to avoid being hit by them.

Isolation: So one way to avoid feeling the effects of waves in a pond, to use our example, is to build a wall down the center of the pond. As long as there no holes in the wall for the waves to diffract through, you should be able to avoid feeling the effects of the waves. Noise isolation works much the same way. You can use earplugs that are firmly mounted in your ears to form a seal and that should prevent any sound waves from reaching your eardrums, right? Well, not quite. The wrinkle is that sound can travel through solids as well. It’s like we built our wall in our pond out of something flexible, like rubber, instead of something solid, like brick. As waves hit the wall the wall itself will move with the wave and then transmit it to your side. So you may still end up hearing some noises, even with well-fitted headphones.

Techniques: earplugs/earbuds, noise isolating headphone or earbuds, noise-isolating architecture,

Damping: So in our pond example we might imagine doing something that makes it harder for waves to move through the water. If you replaced all the water with molasses or honey, for example, it would take a lot more energy for the sound waves to move through it and they’d dissipate more quickly.

Techniques: acoustic tiles, covering the intervening wall (with a fabric wall-hanging, foam, empty egg cartons, etc.), covering vents, placing a rolled-up towel under any doors, hanging heavy curtains over windows, putting down carpeting

Masking: Another way to avoid noticing our neighbor’s waves is to start making our own waves. We can either make waves that are exactly the same size as our neighbor’s but out of phase (so when theirs are at their highest peak, ours is at our lowest) so they end up cancelling each other out. That’s basically what noise-cancelling headphones do. Or we can make a lot of own waves that all feel enough like our neighbor’s that when thier wave arrives we don’t even notice it. Of course, if the point it to hear no sound that won’t work quite as well. But if the point is to avoid abrupt, distracting changes in sound then this can work quite nicely.

Techniques: Listening to white noise or music, using noise-cancelling headphones or earbuds


So what would I do? Well, first I’d take as many steps as I could to sound-proof my environment. Try to cover as many of the surfaces in your bedroom as in absorbent, ideally fluffy, surfaces as you can. (If it can absorb water it will probably help absorb sound.) Wall hangings, curtains and a throw rug can all help a great deal.

Then you have a couple options for masking. A fan help to provide both a bit of acoustic masking and a nice breeze. Personally, though, I like a white noise machine that gives you some control over the frequency (how high or low the pitch is) and intensity (loudness) of the sounds it makes. That lets you tailor it so that it best masks the sounds that are bothering you. I also prefer the ones with the fans rather than those that loop recorded sounds, since I often find the loop jarring. If you don’t want to or can’t buy one, though, myNoise has a number of free generators that let you tailor the frequency and intensity of a variety of sounds and don’t have annoying loops. (There are a bunch of additional features available that you can access for a small donation as well.)

If you can wear earbuds in bed, try playing a non-distracting noise at around 200-1000 Hertz, which will cover a lot of the speech sounds you can’t easily dampen. Make sure your earbuds are well-fitted in the ear canal so that as much noise is isolated as possible. In addition, limiting the amount of exposed hard surface on them will also increase noise isolation. You can knit little cozies, try to find earbuds with a nice thick silicon/rubber coating or even try coating your own.

By using many different strategies together you can really reduce unwanted noises. I hope this helps and good luck!

Does reading a story affect the way you talk afterwards? (Or: do linguistic tasks have carryover effects?)

So tomorrow is my generals exam (the title’s a bit misleading: I’m actually going to be presenting research I’ve done so my committee can decide if I’m ready to start work on my dissertation–fingers crossed!). I thought it might be interesting to discuss some of the research I’m going to be presenting in a less formal setting first, though. It’s not at the same level of general interest as the Twitter research I discussed a couple weeks ago, but it’s still kind of a cool project. (If I do say so myself.)

Plush bunny with headphones.jpg

Shhhh. I’m listening to linguistic data. “Plush bunny with headphones”. Licensed under Public Domain via Wikimedia Commons.

Basically, I wanted to know whether there are carryover effects for some of the mostly commonly-used linguistics tasks. A carryover effect is when you do something and whatever it was you were doing continues to affect you after you’re done. This comes up a lot when you want to test multiple things on the same person.

An example might help here. So let’s say you’re testing two new malaria treatments to see which one works best. You find some malaria patients, they agree to be in your study, and you give them treatment A and record thier results. Afterwards, you give them treatment B and again record their results. But if it turns out that treatment A cures Malaria (yay!) it’s going to look like treatment B isn’t doing anything, even if it is helpful, because everyone’s been cured of Malaria. So thier behavior in the second condition (treatment B) is affected by thier participation in the first condition (treatment A): the effects of treatment A have carried over.

There are a couple of ways around this. The easiest one is to split your group of participants in half and give half of them A first and half of them B first. However, a lot of times when people are using multiple linguistic tasks in the same experiment, then won’t do that. Why? Because one of the things that linguists–especially sociolinguists–want to control for is speech style. And there’s a popular idea in sociolinguistics that you can make someone talk more formally, but it’s really hard to make them talk less formally. So you tend to end up with a fixed task order going from informal tasks to more formal tasks.

So, we have two separate ideas here:

  • The idea that one task can affect the next, and so we need to change task order to control for that
  • The idea that you can only go from less formal speech to more formal speech, so you need to not change task order to control for that

So what’s a poor linguist to do? Balance task order to prevent carryover effects but risk not getting the informal speech they’re interested in? Or keep task order fixed to get informal and formal speech but at the risk of carryover effects? Part of the problem is that, even though they’re really well-studied in other fields like psychology, sociology or medicine, carryover effects haven’t really been studied in linguistics before. As a result, we don’t know how bad they are–or aren’t!

Which is where my research comes in. I wanted to see if there were carryover effects and what they might look like. To do this, I had people come into the lab and do a memory game that involved saying the names of weird-looking things called Fribbles aloud. No, not the milkshakes, one of the little purple guys below (although I could definitely go for a milkshake right now). Then I had them do one linguistic elicitation tasks (reading a passage, doing an interview, reading a list of words or, to control for the effects of just sitting there for a bit, an arithmetic task). Then I had them repeat the Fribble game. Finally, I compared a bunch of measures from speech I recorded during the two Fribble games to see if there was any differences.

Greeble designed by Scott Yu and hosted by the Tarr Lab wiki (click for link).

Greeble designed by Scott Yu and hosted by the Tarr Lab wiki (click for link).

What did I find? Well, first, I found the same thing a lot of other people have found: people tend to talk while doing different things. (If I hadn’t found that, then it would be pretty good evidence that I’d done something wrong when designing my experiment.) But the really exciting thing is that I found, for some specific measures, there weren’t any carryover effects. I didn’t find any carryover effects for speech speed, loudness or any changes in pitch. So if you’re looking at those things you can safely reorder your experiments to help avoid other effects, like fatigue.

But I did find that something a little more interesting was happening with the way people were saying their vowels. I’m not 100% sure what’s going on with that yet. The Fribble names were funny made-up words (like “Kack” and “Dut”) and I’m a little worried that what I’m seeing may be a result of that weirdness… I need to do some more experiments to be sure.

Still, it’s pretty exciting to find that there are some things it looks like you don’t need to worry about carryover effects for. That means that, for those things, you can have a static order to maintain the style continuum and it doesn’t matter. Or, if you’re worried that people might change what they’re doing as they get bored or tired, you can switch the order around to avoid having that affect your data.

Tweeting with an accent

I’m writing this blog post from a cute little tea shop in Victoria, BC. I’m up here to present at the Northwest Linguistics Conference, which is a yearly conference for both Canadian and American linguists (yes, I know Canadians are Americans too, but United Statsian sounds weird), and I thought that my research project may be interesting to non-linguists as well. Basically, I investigated whether it’s possible for Twitter users to “type with an accent”. Can linguists use variant spellings in Twitter data to look at the same sort of sound patterns we see in different speech communities?

Picture of a bird saying

Picture of a bird saying “Let’s Tawk”. Taken from the website of the Center for the Psychology of Women in Seattle. Click for link.

So if you’ve been following the Great Ideas in Linguistics series, you’ll remember that I wrote about sociolinguistic variables a while ago. If you didn’t, sociolinguistic variables are sounds, words or grammatical structures that are used by specific social groups. So, for example, in Southern American English (representing!) the sound in “I” is produced with only one sound, so it’s more like “ah”.

Now, in speech these sociolinguistic variables are very well studied. In fact, the Dictionary of American Regional English was just finished in 2013 after over fifty years of work. But in computer mediated communication–which is the fancy term for internet language–they haven’t been really well studied. In fact, some scholars suggested that it might not be possible to study speech sounds using written data. And on the surface of it, that does make sense. Why would you expect to be able to get information about speech sounds from a written medium? I mean, look at my attempt to explain an accent feature in the last paragraph. It would be far easier to get my point across using a sound file. That said, I’d noticed in my own internet usage that people were using variant spellings, like “tawk” for “talk”, and I had a hunch that they were using variant spellings in the same way they use different dialect sounds in speech.

While hunches have their place in science, they do need to be verified empirically before they can be taken seriously. And so before I submitted my abstract, let alone gave my talk, I needed to see if I was right. Were Twitter users using variant spellings in the same way that speakers use different sound patterns? And if they are, does that mean that we can investigate sound  patterns using Twitter data?

Since I’m going to present my findings at a conference and am writing this blog post, you can probably deduce that I was right, and that this is indeed the case. How did I show this? Well, first I picked a really well-studied sociolinguistic variable called the low back merger. If you don’t have the merger (most African American speakers and speakers in the South don’t) then you’ll hear a strong difference between the words “cot” and “caught” or “god” and “gaud”. Or, to use the example above, you might have a difference between the words “talk” and “tock”. “Talk” is little more backed and rounded, so it sounds a little more like “tawk”, which is why it’s sometimes spelled that way. I used the Twitter public API and found a bunch of tweets that used the “aw” spelling of common words and then looked to see if there were other variant spellings in those tweets. And there were. Furthermore, the other variant spellings used in tweets also showed features of Southern American English or African American English. Just to make sure, I then looked to see if people were doing the same thing with variant spellings of sociolinguistic variables associated with Scottish English, and they were. (If you’re interested in the nitty-gritty details, my slides are here.)

Ok, so people will sometimes spell things differently on Twitter based on their spoken language dialect. What’s the big deal? Well, for linguists this is pretty exciting. There’s a lot of language data available on Twitter and my research suggests that we can use it to look at variation in sound patterns. If you’re a researcher looking at sound patterns, that’s pretty sweet: you can stay home in your jammies and use Twitter data to verify findings from your field work. But what if you’re not a language researcher? Well, if we can identify someone’s dialect features from their Tweets then we can also use those features to make a pretty good guess about their demographic information, which isn’t always available (another problem for sociolinguists working with internet data). And if, say, you’re trying to sell someone hunting rifles, then it’s pretty helpful to know that they live in a place where they aren’t illegal. It’s early days yet, and I’m nowhere near that stage, but it’s pretty exciting to think that it could happen at some point down the line.

So the big take away is that, yes, people can tweet with an accent, and yes, linguists can use Twitter data to investigate speech sounds. Not all of them–a lot of people aren’t aware of many of their dialect features and thus won’t spell them any differently–but it’s certainly an interesting area for further research.