Author: Caitlin O'Connell

The public representation of homosexual men in seventeenth-century England – a corpus based view

Baker and McEnery (2017) wanted to find out what the public representation of gay men was in the 1700’s. Of course they weren’t called “gay men” back then and there was a broad range of male-on-male activity that guys could engage in to be considered anything from a sinner to a sorcerer. So this is actually a look at the public representation of guys who did what Jonathan Van Ness would call “gay stuff.”

Unfortunately this study only covers gay men because of how little writing exists about other queer people from that very binary time. The approach was to explore how gay men were written about. And let’s remember that gayness wasn’t just taboo or frowned upon, it was a capital offense and was only legalized in the UK in 1967.

Source

They used the Early English Books Online Corpus version 3 (EEBO v3), which is great, but unfortunately it’s got so much religious stuff (from meeting minutes to plays and journalism) that results are a bit lopsided.

Challenges

As mentioned above, the large number of religious texts skewed the results. For example sodomite is by far the most frequent term, but it’s mostly used in a Bible-y context (you know, the whole Sodom and Gomorrah thing). The word collocates the most consistently with Genesis, filthy and some guy called Lot because the Sodom and Gomorrah story was in a bit of the Bible called Genesis and the city Sodom had the cute nickname Filthy Sodom. And also Lot was there, I guess. In the Bible-y sense, the word  connoted wickedness, sin, and other deeply negative things, but not necessarily gay stuff. So none of that information is particularly relevant to the public perception of gay men in the 1700’s.

Just as an interesting side note, the word sodomite declined in usage over the century while at the same time there was a rise in church doubt and anti-catholic writings. Also, sodomite collocates with harlot and whore, the only apparent link to sex of any kind.

The other thing is that gay-stuff was just really the most marginal. There was a ton of censorship, with trial records being destroyed and there’s no evidence in the EEBO-v3 of any man self-identifying as ‘into dudes’ because they could have been imprisoned, had their wealth seized, or even been put to death. So what remains in writing is heavily prejudiced, negative, religious, based in mythology, and controlled by the homophobic patriarchy.

Finally there’s the problem of the searching part. Like, what were they to even search the corpus for? They couldn’t search any of our modern terms like homosexual, gay or queer, so then what? What they did was familiarize themselves with the corpus and use their own knowledge and words from the Lexicon of Early Modern English (LEME) and the Historical Thesaurus of the Oxford English Dictionary. They also found more words as they went through. Armed with all the terms for homosexuals and male prostitutes who serviced men they could find, they dove in.

What I did

I took all the words McEnery and Baker searched for and all the words they found in EEBO-v3 and presented them in a dictionary format to accompany this post (click here for dictionary). When possible, I’ve included the metadata from the paper like frequency in the EEBO-v3, era, and definition. From my own brain parts I contributed part of speech and pronunciation. The definitions are those that Baker and McEnery arrived at through collocational analysis. Those without definitions weren’t found in the corpus or weren’t used in a way that allowed for analysis.

Example:

² High Frequency is greater than 500 hits in EEBO-v3, Mid Frequency is between 500 and 100, Low Frequency is from 10 to 100, and Infrequent is anything fewer than 10.

Side note: My intention is for this to be fun because some of the words sound ridiculous to our 21st Century ears (he-strumpet comes to mind), but I would like to acknowledge that none of these were kind-hearted terms. They represent oppression and hate written into law. These laws penalized anyone the cis-gendered heter-normative patriarchy found threatening. I went into this study with a love for lexicography, polysemy, and history, but it’s impossible to explore all of these words without experiencing a deep sadness and regret for the centuries of suffering these words represent.

Conclusion

Seems like only people who thought homosexuality was deviant wrote about it and wrote meanly so. There isn’t a single self-referential use of any of these terms in the whole corpus. However, it is definitely interesting that sexual orientation was at least referenced because there are scholars who claim that homosexuality wasn’t conceivable at that time. These words seem to argue against that.

Also cool is that there are so many different terms. Which to me says that there wasn’t just one concept of a man who was into “gay stuff,” but a variety of different ways to get involved. Sodomy could lead to execution, but ganymede and catamite weren’t accompanied by legal sentences. My favorite realization is that effeminacy wasn’t considered an indicator of sexuality. Apparently, it began to be associated with male homosexuality in the next century at which time guys who were afraid of retribution had to stop kissing each other in greetings and holding hands in public. Finally, it’s interesting that foreign languages and ancient Greek and Roman sources played a big role. And many authors described “these people” and “their acts” as being outside of England. So xenophobic.

Baker and McEnery have one final note for corpus linguists: get back to the text and get into concordancing. It’s called close reading and it involves looking beyond your five word context. Try it. I know I will be.

This article is great for lexicography bishes, history bishes, corpus bishes, and queer bishes.

Click here to proceed to the Dictionary of 17th Century Terms for Homosexual Men

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Mcenery, Tony, and Helen Baker. “The Public Representation of Homosexual Men in Seventeenth-Century England – a Corpus Based View.” Journal of Historical Sociolinguistics, vol. 3, no. 2, Jan. 2017, doi:10.1515/jhsl-2017-1003.

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Maybe it’s a grime [t]ing: TH-stopping among urban British youth

I’ve been thinking a lot lately about how identity is something that we perform. I was introduced to this idea through my exploration of the Iggy Azalea’s persona and performance for my first Linguabishes post (here). It was my first glimpse at the tricky area of identity research. Not dissimilar from code-switching, your identity performance at work is probably super different from the one you perform to your bishes. Identity can change from context to context and it depends on your audience.

Identity is complex and luckily it evolves. Imagine if you were currently performing your identity from age 15.

In Rob Drummond’s recent paper, “Maybe it’s a grime [t]ing: TH-stopping among urban British youth” he cites Bucholtz & Hall’s (2010:19–25) five principles of identity. The gist of which is that identities are not fully-formed, they’re not explicitly conceived, and they’re dynamic.

Adolescence is a time of emerging identities. One way teens attempt to craft their identities is by emulating their role models. Maybe you were Spice Girls fan in 1997 and tried out your first British accent, or an emo Avril Lavigne fan in 2002 and decided to go out and get a bunch of eyeliner. These would both be conscious attempts to appear to be in the same group or have a similar identity as your role models, but remember identity performance isn’t always a conscious choice.

When Drummond was working on the UrBEn-ID (Urban British English and Identity) Project in Manchester (the one in the UK, ok bishes?), he noticed something interesting about 4 students who liked a specific kind of music: they performed TH-stopping some of the time.

TH-stopping is pronouncing a voiceless th as a t, like ‘thing’ as ‘ting’. While less uncommon than  its voiced sister, DH-stopping, (pronouncing ‘them’ as ‘dem’), it occurs in many English varieties including West Indian Englishes and Creoles, Jamaican Creole, British Creole, Irish English, and Liverpudlian. It is also associated with AAE, so in it can be found in Hip-Hop and Grime.

Have you heard of Grime? It’s a type of music born out of early 2000’s East London. Think Fix up, Look Sharp. Grime, like Hip-Hop is rooted in urban black culture, but blooming out of East London, it is also cross-racial using a multiethnolect, an ethnically neutral dialect, called Multicultural London English (MLE). More on that (in search of a Multicultural Urban British English (MUBE)).

A lot of previous work has looked at the language-ethnicity link. Does language reflect ethnicity? Or is it a social performance of ethnicity? I guess no one’s really all that sure, but in this specific case, Drummond found that ethnicity was most definitely not a factor.

While most research that looks at identities of adolescents is in mainstream schools like Eckert’s research, the adolescents in this study were four boys outside of the mainstream education system. They attended a specialized learning center that was designed for students who didn’t fit into the mainstream system for a variety of reasons. The study took place over 2 years and had 25 participants, but TH-stopping was in such limited use that only these 4 boys stood out. To find out why they were TH-stopping they look at a whole bunch of different variables including sex, ethnicity, speech context, musical tastes, age, and a bunch more. Which variable stood out may surprise you…

While context was a significant factor (meaning that in a mock job interview TH-stopping didn’t occur), the biggest variable turned out to be music, but not reported taste in music. Specifically, it was whether the subject was observed to be rapping in class. For the 3 out of the 4 boys, rapping is almost a feature of speech since they regularly slip in and out of it during conversation.

The 4 boys used TH-stopping in conversations where they were trying to show ingroup status with the street, urban, tough culture embodied by Grime. One example is a conversation they had about a mutual acquaintance who was about to get out of jail. They were each trying to show that this person was a friend of theirs. They each in turn referred to him as a tief (for ‘thief’). Another example is of a different boy who in the context of discussing his favorite Grime artist does not TH-stop and then self-corrects in order to use it.

Drummond concludes that among the subjects in this study TH-stopping is not a marker of ethnicity, but a part identity performance. It is a “linguistic resource” that helps align them with a general sense of tough or street culture embodied by grime.

 

 

And just to be clear, it’s not like listening to this type of music has caused their dialects to change. It’s that in order to show that they live in the Grime world, they occasionally stop a TH and perform in-groupedness. This is the major take-away. That and the fact that ethnicity as a concept is not a meaningful mechanism for grouping people.

This should be taken into account in future studies that attempt to link identity and language.

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Drummond, Rob. “Maybe Its a Grime [t]Ing: Th-Stopping among Urban British Youth.” Language in Society, vol. 47, no. 02, 2018, pp. 171–196., doi:10.1017/s0047404517000999.

Eckert, Penelope. “Linguistic Variation as Social Practice: The Linguistic Construction of Identity in Belten High (Review).” Language, vol. 77, no. 3, 2001, pp. 575–577., doi:10.1353/lan.2001.0193

 

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Are Emojis Predictable?

Emojis are cool, right? Well typing that sure didn’t feel cool, but whatever. The paper “Are Emojis Predictable?” by Francesco Barbieri, Miguel Ballesteros, Horacio Saggion explores the relationships between words and emojis by creating robot-brains that can predict which emojis humans would use in emoji-less tweets.

But, what exactly are emoji (also is the plural, emoji, or emojis?) and how do they interact with our text messaging? Gretchen McCulloch says you can think about them like gestures. So if I threaten you by dragging a finger across my throat IRL, a single emoji of a knife might do the trick in a text. But if they act like gesture in some cases, what are we to make of the unicorn emoji? Or the zombie? It‘s not representative of eating brains right? Right?? Tell me the gesture isn’t eating brains!

So, obviously,  trying to figure out what linguistic roles emoji can play is tough and it doesn’t help that they haven’t been studied all that much from an Natural Language Processing (NLP) perspective. Not to mention the perspective of AI. Will emoji robots take over the world like that post-apocalyptic dystopian hellscape depicted in movies like… the Emoji Movie and…Lego Batman? Studying emojis will not only protect us from the emoji-ocalypse, but also help analyze social media content and public opinion. That’s called sentiment analysis btw, but more on all things I just tried to learn later.

The Study (or Machine Learning Models, oh my 😖)

For this study, the researchers (from my alma mater, Universitat Pompeu Fabra) used the Twitter APIs to determine the 20 most frequently used emojis from 40 million tweets out of the US between Oct 2015 and May 2016. Then they selected only those tweets that had a single emoji from the top 20 list. It was more than 584600 tweets. Then they removed the emoji from the tweet and trained machine learning models to predict which it was. Simple, right?

Now just to be clear, the methods in this study are way above my head. I don’t want anyone confusing me for someone who understands exactly what went on here because I was fully confused through the entire methods section. I tried to summarize what little understanding I think I walked away with, but found there was just way too much content. So here is a companion dictionary of terms for the most computationally thirsty bishes (link).

So actually two experiments were performed. The first was comparing the abilities of different machine learning models to predict which emoji should accompany a tweet. And the second was comparing the performance of the best model to human performance.

The Robot Face-Off (🤖 vs 🤖)

In the first experiment, the researchers removed the emoji from each tweet. Then they used 5 different models (see companion dictionary for more info) to predict what the emoji had been:

  1. A Bag of Words model
  2. Skip-Gram Average model
  3. A bidirectional LSTM model with word representations 
  4. A bidirectional LSTM model with character-based representations 
  5. A skip-gram model trained with and without pre-trained word vectors

They found that the last three (the neural models) performed better than the first two (the baselines). From this they drew the conclusion that emoji collocate with specific words. For example, the word love collocates with ❤. I’d also like to take a moment to point out this study which points out the emojis are mostly used with words and not to replace them. So we’re more likely to text “I love you ❤” than “I ❤ you.”

 

The Best “Robot”

The best performing model was the char-BLSTM with pretrained vectors on the 20-emojis. Apparently frequency has a lot to do with it. It shouldn’t be surprising that the model predicts the most frequent emojis more frequently. So in a case where the word love is used with the 💕, the model would prefer ❤. Also the model confuses emojis that are used in high frequency and varied contexts. 😂 and 😭 are an example of this. They’re both used in contexts with a lot of exclamation points, lols, hahas, and omgs and often with irony.

The case of 🎄 was interesting. There were only 3 in the test set and the model correctly predicted it in the two occasions where the word Christmas was in the tweet. The one case without it didn’t get the correct prediction from the model.

Second experiment: 🙍🏽vs 🤖

The second experiment was to compare human performance to the character-based representation BLSTM. These humans were asked to read a tweet with the emoji removed and then to guess which emoji of five emojis (😂, ❤, 😍, 💯, 🔥and ) fit.

They crowdsourced it. And guess what? The char-BLSTM won! It had a hard time with 😍 and 💯 and humans mainly messed up 💯 and 🔥. For some reasons, humans kept putting in 🔥 where it should have been 😂. Probably the char-BLSTM didn’t do that as much because of its preference for high frequency emojis.

5 Conclusion

  • The BLSTMs outperformed the other models and the humans. Which sounds a lot like a terminator-style emoji-ocalypse to me. This paper not only suggests that an automatic emoji prediction tool can be created, but also that it may predict emojis better than humans can and that there is a link between word sequences and emojis. But because different communities use them differently and because they’re not usually playing the role of words necessarily, it’s excessively difficult to define their semantic roles not to mention their “definitions.” And while there are some lofty attempts (notably Emojipedia and The Emoji Dictionary) to “define” them, the lack of consensus makes this basically impossible for the vast majority of them.

I recommend this article to emoji kweens,  computational bishes 💻, curious bishes 🤔, and doomsday bishes 🧟‍♀️.

Thanks to Rachael Tatman for her post “How do we use Emoji?” for bringing some great research to our attention. If you don’t have the stomach for computational methods, but care about emojis, then definitely check out her post.

 


 

Barbieri, Francesco, et al. “Are Emojis Predictable?” Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 2017, doi:10.18653/v1/e17-2017.

Dürscheid, C., & Siever, C. M. (2017). “Beyond the Alphabet–Communication of Emojis” Kurzfassung eines (auf Deutsch) zur Publikation eingereichten Manuskripts.

Tatman, Rachael. “How Do We Use Emoji?” Making Noise & Hearing Things, 22 Mar. 2018, makingnoiseandhearingthings.com/2018/03/17/how-do-we-use-emoji/.

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Companion to “Are Emojis Predictable?”

Welcome to the companion to

Are Emojis Predictable?

by  Francesco Barbieri, Migual Ballesteros, and Horacio Saggion.

This is where I’ve attempted to provide some semblance of explanation for the methods of the study. Look, I tried my best with this, so don’t judge. I ordered it in terms of the difficulty had instead of alphabetically. References at the end for thirsty bishes who just can’t get enough.

Difficulty NLP Model or Term
 Grinning Face on Twitter Sentiment Analysis

A way of determining and categorizing opinions and attitudes in a text using computational methods. Also opinion mining.

 Smiling Face on Twitter Neural Network

A computer network that’s based on how the human brain works.

 Slightly Smiling Face on Twitter Recurrent Neural Network

A type of neural network that at can be trained by algorithms and that stores information to make context-based predictions. Also RNN.

 Slightly Smiling Face on Twitter Bag of Words

A neural network that basically counts up the number of instances of words in a text. It’s good at classifying texts by word frequencies, but because it determines words by the white space surrounding them and  disregards grammar and word order, phrases lose their meaning. Also BoW.

 Neutral Face on Twitter Skip Gram

A neural network model does the opposite of the BoW. Instead of looking at the whole context, the skip gram considers word pairs separately. It’s trying to predict the context from a word, so it weighs closer words more than further ones. So the order of words is actually relevant. Also Word2Vec.

 Neutral Face on Twitter Long Short-term Memory Network

A recurrent neural network that can learn the orders of items in sequences and so can predict them. Also LSTM.

 Expressionless Face on Twitter Bidirectional Long Short-term Memory Network

The same as above, but it’s basically time travel because half the neurons are searching backwards and half are searching forwards even if more items are added later. Also BLSTM.

 Downcast Face With Sweat on Twitter Char-BLSTM

A character-based approach that learns representations for words that look similar, so it can handle alternatives of the same word type. More accurate than the word-based variety.

 Confounded Face on Twitter Word-BLSTM

Some kind of word-based variant of the above? Probably?

 Face Vomiting on Twitter Word Vector

Ya, this one is umm… well, you see, it has magnitude and direction. And like, you have to pre-train it. So… “Fuel your lifestyle with .”

Congratulations if you’ve made it this far! You probably already know more than me. Scream it out. I know I did 🙂

 


 

REFERENCES

Bag of Words (BoW) – Natural Language Processing, ongspxm.github.io/blog/2014/12/bag-of-words-natural-language-processing/.

Britz, Denny. “Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs.” WildML, 8 July 2016, www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/.

Brownlee, Jason. “A Gentle Introduction to Long Short-Term Memory Networks by the Experts.” Machine Learning Mastery, 19 July 2017, machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/.

Brownlee, Jason Brownlee. “A Gentle Introduction to the Bag-of-Words Model.” Machine Learning Mastery, 21 Nov. 2017, machinelearningmastery.com/gentle-introduction-bag-words-model/.

Chablani, Manish. “Word2Vec (Skip-Gram Model): PART 1 – Intuition. – Towards Data Science.” Towards Data Science, Towards Data Science, 14 June 2017, towardsdatascience.com/word2vec-skip-gram-model-part-1-intuition-78614e4d6e0b.

Verwimp, et al. “Character-Word LSTM Language Models.” [1402.1128] Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition, Cornell University Library, 10 Apr. 2017, arxiv.org/abs/1704.02813.

Colah, Christopher. “Understanding LSTM Networks.” Understanding LSTM Networks — Colah’s Blog, colah.github.io/posts/2015-08-Understanding-LSTMs/.

Nielsen. “Neural Networks and Deep Learning.” Neural Networks and Deep Learning, Determination Press, 1 Jan. 1970, neuralnetworksanddeeplearning.com/chap1.html.

“Sentiment Analysis: Concept, Analysis and Applications.” Towards Data Science, Towards Data Science, 7 Jan. 2018, towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17.

gk_. “Text Classification Using Neural Networks – Machine Learnings.” Machine Learnings, Machine Learnings, 26 Jan. 2017, machinelearnings.co/text-classification-using-neural-networks-f5cd7b8765c6.

Thireou, T., and M. Reczko. “Bidirectional Long Short-Term Memory Networks for Predicting the Subcellular Localization of Eukaryotic Proteins.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 3, 2007, pp. 441–446., doi:10.1109/tcbb.2007.1015.

“Vector Representations of Words  | TensorFlow.” TensorFlow, www.tensorflow.org/tutorials/word2vec.

“Word2Vec Tutorial – The Skip-Gram Model.” Word2Vec Tutorial – The Skip-Gram Model · Chris McCormick, mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/.

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Gendered representations through speech: The case of the Harry Potter series

In this paper Dr. Eberhardt (2017) looks at the way gender is represented in the Harry Potter series (the books) by comparing the verbs used to report the speech of Harry’s two sidekicks, Hermione and Ron. She found that even though the words used to report their speech are largely the same, subtle patterns revealed negative gender stereotypes.

Background

There is a really popularly held notion that gender determines the way we speak. Luckily, sociolinguists don’t really follow this line of thinking anymore. Instead they ask how gender and identity interact with a bunch of different of aspects. Unluckily, outside of linguistics, this Men-are-from-Mars BS is so popular that it has seeped into to the subconsciouses of even writers like J.K. Rowling who are attempting to create feminist characters.

And guess what, Eberhardt points out that men and women pretty much use language the same way. Also this fun feminist-side-note, did you know about the semantic degradation of female equivalents of word pairs? And not just word pairs, but also their collocations? And maybe not just in English? It’s a thing. For example, spinster is negative, but bachelor isn’t. It’s called perjoration and it’s interesting. Look it up.

So we can say that language is used to reinforce tired stereotypes, but it’s not language alone because language is culture. So if language is continually depicting women as meek, emotional, or say, shrill (right?), it’s because we’ve all bought into it as a language community.

And as far as children’s literature goes (which btw Harry Potter is despite the number of adults who also enjoy the series), gender representation has a huge impact on how children learn gender-specific behavior. When I was a voracious young reader, I learned I should barely hold back tears, cry out desperately, stammer, and say things breathily to be a proper girl.

No shade on Rowling who wrote a beloved series, created compelling characters and story, and made a massive contribution to culture. However, she has been repeatedly criticized for her failed attempt to create a feminist character. Almost twice as many men are mentioned by name in the series than women¹, some² contend the books reinforce the patriarchy, and others³ think JK’s attempt at gender equality is superficial.

The How of it all

The entire set of seven books comprises a corpus of 1.1 million words. Eberhardt looked at all the times Hermione and Ron spoke and checked out what verbs were used to describe their speech. As it turns out, Ron has only a smidge more reporting verbs (2154) than Hermione (1937). However, only Hermione: cried, shrieked, ordered, and screamed and her neutral speech is described differently.

Unique verbs

A closer look at the verbs that were unique to Ron and Hermione revealed that all but one of Hermione’s unique verbs are stereotypes. They’re either high-pitched fear or sadness slash helplessness. Ten points if you can name three from each category (key at the end). Ron’s are either loud (bellow, roar) or emotionally distant (mumble, grumble, grunt). Ron shouts and yells, Hermione gasps and snaps. He mutters, but she whispers. So ya, they’re both super reinforcing of stereotypes.

When a character uses cry for a magical incantation, it suggests that the spell was performed in a loud,emotional, high-pitched voice. Ron only does this once. Hermione uses it 37 times for both spell casting and, more frequently, for emotion. This frequency increases throughout the series. So boys are angry and loud and girls are increasingly upset as they mature and become sexually viable.

Another way to look at the reporting verbs in the novels is to check out which verbs both Hermione and Ron share, but which, like cry, are used different amounts. Eberhardt found that both characters suggest and demand but with inverse frequencies. So he demands more than twice as much as she does and she suggests twice as much as he does. Probably because men are assertive and women are cooperative, right?

Modified speech

Apart from their unique verbs, Eberhardt also noticed a difference in the way their verbs were described. Not only is Hermione’s speech described in more detail than Ron’s, but she also has fewer neutral reporting verbs like, say or ask. Ron’s modifiers show his knowledge, but the modifiers Hermione gets show her feelings and her feelings are often negative. She gets angry, fearful and sometimes says things seriously.

For example, in a heated argument Ron’s speech is reported with said alone, but Hermione’s said is modified by her voice unusually high. Ron’s speech was also reported with loud, violent words like hurled at, and shouted while Hermione’s speech was reported with cry.

In this way we may infer Ron’s emotions from context, but they’re clearly not as important. The same goes for Hermione’s intelligence, she is ostensibly the most knowledgeable of the crew, but that is not as important as her emotions, which get described in detail. Is this in-depth description of Hermione’s emotions a part of the way we feel entitled to scrutinize and judge women’s appearances, voices, and actions? You tell me.

So?

Hermione and Ron are mostly the same, but the areas where they are different are interesting. Are the differences due to their genders or are we to believe that Hermione is just one emotional young woman independent of stereotypes? Maybe if the verbs of the other characters were also examined, we’d find that many more male characters cry and shriek and many female ones mutter, but since the results so closely align with gender stereotypes and the findings of other studies, maybe not.

Also, Eberhardt points out that this binary pattern (Hermione is emotional and Ron rational) supports the theory that women are one thing and men a different thing and presents this belief to young minds. For all the good Rowling’s does in creating a feminist icon, she undoes by instilling this stereotypical ideology in impressionable minds.

Read this article if you’re a sociolinguist bish, a language and gender bish, or a witchy bish.

Key to Hermione’s Unique Verbs

High pitch fear: 

  • scream
  • squeal
  • shriek

Sadness/helplessness: 

  • squeak
  • wail
  • whimper

 

Eberhardt, M. (2017). Gendered representations through speech: The case of the Harry Potter series. Language and Literature,26(3), 227-246. doi:10.1177/0963947017701851

²Dresang, E. (2002). Hermione Granger and the Heritage of Power. In The Ivory Tower and Harry Potter: Perspectives on a Literary Phenomenon(p. 211).

¹Heilman, E., & Donaldson, T. (2009). Representations of Gender the Harry Potter Series. In Critical Perspectives on Harry Potter(p. 139).

³Yeo, M. (2004). Harry Potter and the Chamber of Secrets: Feminist Interpretations/Jungian Dreams. SIMILE: Studies In Media & Information Literacy Education,4(1), 1-10. doi:10.3138/sim.4.1.002

 

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Police interviews with vulnerable people alleging sexual assault: Probing inconsistency and questioning conduct

This paper examines actual police interviews with people with intellectual disabilities reporting sexual assault. Focusing on probing inconsistencies in the victim’s account with pragmatically difficult questions, Antaki, C., Richardson, E., Stokoe, E., & Willott, S. attempt to determine how well officers follow recommended interview guidelines.

It is known that cops have taken insensitive lines of questioning with victims of sexual assault and rape. Stories of victims being asked what they were wearing or if they’d be drinking or other irrelevant questions about the context of their attack are as common as they are infuriating.

Implying fault and questioning the victim’s conduct is not only demoralizing to a person who is already feeling guilt, shame, and fear. Worse, it discredits the victim’s statement for judicial processing. And that’s just for intellectually typical victims who may have the language processing skills to be able to clarify details and defend themselves.

People with intellectual disabilities have even more obstacles to overcome. They are more likely to be victims of abuse and violence, less likely to succeed in prosecuting their assaulters, and suffer greater emotional and psychological distress after the event to boot. This paper doesn’t specify what is meant by “intellectual disability,” except to say that those with intellectual disabilities, learning, or psychiatric problems, can struggle to communicate, function socially, and to read pragmatic linguistic clues (head to the ever current and informative Conscious Style Guide for a brush-up on terms).

Antaki et. al. generously point out that police are in a tough place because they need to be able to present a statement for the victim’s defense in court. This means they need to obtain a clear account of events from a recently traumatized person who may have a hard time discussing their assault or remembering it clearly. If that person also struggles with communication and social functioning, it can be even more difficult to compile a coherent series of events. Toss in a little difficulty reading pragmatic linguistic clues like non-literal expressions and hypothetical and indirect questions (you know, things that exist in typical conversations and interviews) and then think about how well those interviews go.

You might be thinking that given the frequency and severity of sexual assault, cops are probably trained to interview victims. Well, ya… kinda. The police in this study are advised by the Royal College of Psychiatrists to have training for interviewing those with intellectual disabilities and they provide a general guide to help with that. The guide points out that inconsistencies and omissions are usually caused by the interviewer jumping to conclusions. They indicate that cops should never voice suspicion, call the witness a liar, or challenge them directly. The guide is not specific to those with intellectual disabilities, however, and there doesn’t seem to be any mechanism for tracking how well the guide is followed let alone how well it works for those with intellectual disabilities.

The focus of this study is to determine how well actual police interviews adhere to this guide when interviewing people with intellectual disabilities, especially in probing the inconsistencies with pragmatically difficult questions. Evidence was gathered from 19 interviews with people with what the English police force called “learning disabilities” reporting sexual assault or rape. Of the 19 only 3 of them went to court, and only 2 succeeded in getting a guilty verdict.

 

RESULTS

Spoiler alert, there were departures from the guidelines. Mainly in areas the guide explicitly advised against. They were a) implying the story made no sense or was very unlikely or b) implying the witness’ behavior was to blame. These implications involve complex pragmatics that may be difficult for those with intellectual disabilities to process.

Basically, these questions present a logical problem that requires extra processing that people with intellectual disabilities might not be able to handle. Hypothetical phrasings like “If it was raining, why didn’t you bring an umbrella?” cast doubt and indicate failure to do something appropriate, but the interviewee may not pick up on that. Hypothetical questions also require the interviewee to process something that did not happen and is not a part of their memory. On top of that, they need to see that their conduct was unexpected or wrong and detect the implication of blame in order to defend themselves and their credibility. Complicated.

These types of questions challenge the victim’s conduct and truthfulness. This is exactly what the interviewers are asked not to do. The extra stress added by these questions can even impede memory which is why answers to these challenging questions frequently are “I don’t know.” This is a problematic answer since a person is expected to know why they do what they do. Being unable to explain one’s actions is a credibility nightmare.

 

Discussion

As the guide says, asking why causes more problems than it fixes. It promotes the feeling of blame when victims often already blame themselves.

And while it is tough for interviewers because they have to record a first-hand statement as evidence for court and check for inconsistencies and vagueness, in order to serve the victim well, the guidelines need to be taken seriously and adherence to them needs to be monitored.

Without very rigorous training and a high level of language competence, it is unlikely that a police officer, or anyone, would have the skills to identify the pragmatic aspects of their own speech or to consider the pragmatic capacity of those with intellectual disabilities.

Even though this study is based on a small sample size, Antaki et al. recommend avoiding probing especially with the hypothetical “Why didn’t you X?”. That seems reasonably obvious, but beyond that there needs to be a robust system for identifying the needs of a victim. Descriptions made by the police of the victim’s disability were cursory. Labels like “learning disability” or “deaf” aren’t helpful or informed assessments.

Finally, interviewing is a skill and those doing it need to be highly trained to serve the victim and their specific needs. That could mean teaching some basic pragmatics to officers so they can avoid complex logical problems, bringing experienced linguists onto the force, or other better ideas I haven’t thought of. The actual application of applied linguistics to interviews could be the difference between putting a sex-offender behind bars or back on the street.

This article is great for pragmatics and sociolinguistics bishes or bishes interested in discourse analysis. There’s even a fun smidgen of Wh-movement and NPI licensing for my syntax bishes.


Antaki, C., Richardson, E., Stokoe, E., & Willott, S. (2015). Police interviews with vulnerable people alleging sexual assault: Probing inconsistency and questioning conduct. Journal of Sociolinguistics,19(3), 328-350. doi:10.1111/josl.12124

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‘First things first, Im the realest’: Linguistic appropriation, white privilege, and the hip-hop persona of Iggy Azalea

Although Iggy Azalea’s infamous demise makes her a dated reference for cultural appropriation,  Maeve Eberhardt and Kara Freeman’s thorough linguistic analysis in this paper is incredibly relevant in 2018. Just because Azalea went away certainly doesn’t mean ignorance and privilege did. Considering the number of white artists currently successfully monetizing hip-hop and black culture, I thought it was a good time to look at performance, persona, and linguistic blackface.

Eberhardt and Freeman provide a description of the state of white artists in hip-hop. They point out that since hip-hop’s popularity is global, white people now comprise the majority of listeners. However, their participation in the industry (and consequent success) has led to a lot of distrust from the hip-hop community. There have been many white rappers who have successfully avoided minstrelsy and mimicry by staying true to their own language backgrounds. These are artists who do not try to prove their validity as members of the hip-hop community.

Eberhardt and Freeman point out, for example, that the Beastie Boys had a specific high pitch and tonal quality that marked their whiteness and Eminem frequently refers to being white and only uses the linguistic features he grew up with. In short, while these artists may use normal code-switching, they never completely cross over into linguistic varieties from groups they don’t belong to.

Many white users of African American English (AAE) perform isolated linguistic forms, but ignore AAE’s grammatical rules. It’s Eberhardt and Freeman’s opinion that at best, an I be like here or a saucin’ there, when not commodified, isn’t a big deal, but at worst, it supports the racist status quo in the United States and is no different from the original blackface minstrels who perpetuated negative black stereotypes.

Remember Vanilla Ice? He created a fake “ghetto” background in order to endear himself to the hip-hop community. That was crossing and so was Azalea’s stage persona. Being not only white, but also Australian, she tried to flash her blaccent like a VIP member card. Ultimately though, it was rejected.

Azalea seems to be vying more for approval from the hip-hop community than most.

Freeman and Eberhardt’s study looked at Azalea’s entire discography of five albums from 2011-2014. The lyrics of all 48 songs were compared to her language use in five radio interviews.

They found a number of linguistic features in Azalea’s raps that show a near-native proficiency in AAE. She uses more forms than out-group speakers typically are able to gain from popular media alone. This sets her apart from other white artists who use AAE features. Meghan Trainor or Miley Cyrus, for example, dot their performances with just a few isolated features, but don’t use full native-like sentences. To Eberhardt and Freeman Azalea seems to be vying more for approval from the hip-hop community than most.

Phonologically, the features in Azalea’s music are consistent with southern US rap like her mentor, T.I. She performs morphosyntactic features like the habitual be (“My chat room be popping”), which white performers rarely use successfully, like a native speaker. On top of that, she not only uses current popular slang, but also more permanent non-regional lexical items like finna, grown, and thick to name a few.

One striking feature of Azalea’s performances is her copular absence, also an AAE feature. Compared to four other artists (3 black and 1 white) her copular absence is the second highest. The lowest is Eminem, despite growing up in the US with exposure to AAE. Azalea, who lived in Australia until she was 16 years old and only had mediated access to AAE in her childhood, uses copular absence at a comparable rate to the black rappers analyzed.

Figure 1 – Comparison of copula absence among five artists’ lyrics

 

In interviews, however, she has no copula absence. While it is not uncommon for rappers to code-switch between their musical performances and their radio interviews, Azalea goes further completely crossing from being a native speaker of AAE in music and a native speaker of Australian English in interviews.

Figure 2 – Comparison of copula absence among five artists’ interview speech

 

Beyond her blaccent, the content of her lyrics promotes many stereotypes including hyper-sexuality. When black women declare their bodies attractive, it subverts societal beauty standards. When Azalea, as the accepted archetype of beauty does this, she does not subvert standards, but supports them. In one interview she declares “everybody loves a pretty white girl” in admission of the fact that her appearance was not an obstacle to her success in hop-hop. Oh ya, she also has a lyric about being a slave master. Statements like this show that she is completely unaware of the importance of race in the US.

The linguistic analysis done by Freeman and Eberhardt revealed that Azalea’s mimicry of AAE exceeded that of black rappers. She overshot her attempt to appear authentic and completely missed the point. Using a fake accent to rap about tired stereotypes instead of her own personal experiences was inauthentic and ultimately led to her demise.

My major takeaway is that Iggy Azalea’s “overzealousness” as Eberhardt and Freeman put it, made her stand out from a crowd of artists who appropriate in smaller units. It’s easy to recognize when someone takes black cultural wholesale, but this paper is a good reminder to watch out for those who may be slipping just under the radar. To be sure, there are white girls appropriating black culture all around us.

This article is great for phonology bishes, dialectology bishes, and sociolinguistics bishes


 

Eberhardt, M., & Freeman, K. (2015). ‘First things first, Im the realest’: Linguistic appropriation, white privilege, and the hip-hop persona of Iggy Azalea. Journal of Sociolinguistics,19(3), 303-327. doi:10.1111/josl.12128

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