Theories are tools for communication

I’ve written in the past about instrumentalism, the scientific practice of treating theories as tools that can be evaluated by their usefulness, rather than as claims that can be evaluated as true or false. If you haven’t tried this way of looking at science, I highly recommend it! But if theories are tools, what are they used for? What makes a theory more or less useful?

The process of science starts when someone makes an observation about the world. If we don’t understand the observation, we need to explore more, make more observations. We make hypotheses and test them, trying to get to a general principle that we can apply to a whole range of situations. We may then look for ways to apply this principle to our interactions with the world.

At every step of this process there is communication. The person who makes the initial observation, the people who make the further observations, who make the hypotheses, who test them, who who generalize the findings, who apply them: these are usually multiple people. They need to communicate all these things (observations, hypotheses, applications) to each other. Even if it’s one single person who does it all end to end, that person needs to communicate with their past and future selves, in the form of notes or even just thinking aloud.

These observations, hypotheses and applications are always new, because that’s what science is for: processing new information. It’s hard to deal with new information, to integrate it with the systems we already have for dealing with the world. What helps us in this regard are finding similarities between the new information and things we already know about the world. Once we find those similarities, we need to record this for our own reference and to signal it to others: other researchers, technologists and the rest of the population.

In informal settings, we already have ways of finding and communicating similarities between different observations. We use similes and metaphors: a person’s eyes may be blue like the sky, not blue like police lights. These are not just idle observations, though: the similarities often have implications for how we respond to things. If someone is leaving a job and they say that they’re passing the baton to a new person, they are signaling a similarity between their job and a relay race, and the suggestion is that the new person will be expected to continue towards the same goal the way a relay runner continues along the racecourse.

Theories and models are just formalized versions of metaphors: saying that light is a wave is a way of noting that it can move through the air like a wave moves through water. That theory allowed scientists to predict that light would diffract around objects the way that water waves behave when they encounter objects, a testable hypothesis that has been confirmed. This in turn allowed technologists to design lasers and other devices that took advantage of those wavelike properties, applications that have proven useful.

Here’s a metaphor that will hopefully help you understand how theories are communication tools: another communication tool is a photograph. Sometimes I see a photograph of myself and I notice that I’ve recently lost weight. Let’s say that I have been cutting back on snacks and I see a photo like that. I have other tools for discovering that I’ve lost weight, like scales and measuring tape and what I can observe of my body with my own eyes, but seeing a photo can communicate it to me in a different way and suggest that if I continue cutting back on snacks I will continue to lose weight. Similarly, if I post that photo on Facebook my friends can see that I’ve lost weight and understand that I’m going to continue to cut back on snacks.

A theory is like a photograph in that there is no single best photograph. To communicate my weight loss I would want a photo that shows my full body, but to communicate my feelings about it, a close-up on my face might be more appropriate. Friends of mine who get new tattoos on their legs will take close-ups of the tattoos. We may have six different photos of the exact same thing (full body, face or leg, for example), and be satisfied with them all. Theories are similar: they depend entirely on the purpose of communication.

A theory is like a photograph in that the best level of detail depends on what is being communicated and who the target is. If a friend takes a close-up of four square inches of their calf, that may be enough to show off their new tattoo, but a close-up of four square inches of my calf will probably not tell me or anyone else how much weight I’ve lost. Similarly, if I get someone to take an aerial photograph of me, that may indicate where I am at the time, but it will not communicate much about my weight. This applies to theories: a model with too much detail will simply swamp the researchers, and one with too little will not convey anything coherent about the topic.

A theory is like a photograph in that its effectiveness depends on who is on the other end of the communication. If someone who doesn’t know me sees that picture, they will have no idea how much I weighed before, or that my weight has been affecting my health. They will just see a person, and interpret it in whatever way they can.

A photograph may not be the best way to communicate my weight loss to my doctor. Their methods depend on measurable benchmarks, and they would prefer to see actual measurements made with scales or tape. On the other hand, a photo is a better way to communicate my weight loss to my Facebook friends than posting scale and tape measurements on Facebook, because they (or some of them at least) are more concerned with the overall way I look.

A theory’s effectiveness similarly depends on its audience. Population researchers may be familiar with the theories of Alfred Lotka and Vito Volterra, so if I tell them that ne…pas in French follows a Lotka-Volterra model, they are likely to understand. Chemists have probably never heard of Lotka or Volterra, so if I tell them the same thing I’m likely to get a blank stare.

This means that there is no absolute standard for comparing theories. We are never going to find the best theory. We may be able to compare theories for a particular purpose, with a particular level of detail, aimed at a particular audience, but even then there may be several theories that work about as well.

When I tell people about this instrumental approach to scientific theories and models, some of them get anxious. If there’s no way for theories to be true or false, how can we ever have a complete picture of the universe? The answer is that we can’t. Kurt Gödel showed decades ago with his Incompleteness Theorem that no theory or model can ever completely capture reality, not even a mathematical or computer model. Jorge Luis Borges illustrated it with his story of the map that is the same size as the territory.

Science is not about finding out everything. It’s not about getting a complete picture. That’s because reality is too big and complex for our understanding, or for the formal systems that our computers are based on. It’s just about figuring out more than we knew before. It will never be finished. And that’s okay.

Le Corpus de la scène parisienne

C’est l’année 1810, et vous vous promenez sur les Grands Boulevards de Paris. Vous avez l’impression que toute la ville, voir même toute la France, a eu la même idée, et est venue pour se promener, pour voir les gens et se faire voir. Qu’est-ce que vous entendez?

Vous arrivez à un théâtre, vous montrez un billet pour une nouvelle pièce, et vous entrez. La pièce commence. Qu’est-ce que vous entendez de la scène? Quels voix, quel langage?

Le projet du Corpus de la scène parisienne cherche à répondre à cette dernière question, avec l’idée que cela nous informera sur la première question aussi. Il s’appuie sur les travaux du chercheur Beaumont Wicks et des ressources comme Google Books et le projet Gallica de la Bibliothèque Nationale de France pour créer un corpus vraiment représentatif du langage du théâtre parisien.

Certains corpus sont construits à base d’une «principe d’autorité», qui tend à mettre les voix des aristocrates et des grands bourgeois au premier plan. Le Corpus de la Scène Parisienne corrige ce biais par se baser sur une échantillon tirée au sort. En incorporant ainsi le théâtre populaire, le Corpus de la Scène Parisienne permet au langage des classes ouvrières, dans sa représentation théâtrale, de prendre sa place dans le tableau linguistique de cette période.

La première phase de construction, qui couvre les années 1800 à 1815, a déjà contribué à la découverte des résultats intéressants. Par exemple, dans le CSP en 75% des négations de phrase on utilise la construction ne … pas, mais dans les quatre pièces de théâtre qui font partie du corpus FRANTEXT de la même période, on n’utilise ne … pas qu’en 49% des négations de phrase.

En 2016 j’ai créé un dépôt sur GitHub et commencé à y mettre les textes de la première phase en format HTML. Vous pouvez en lire pour vous amuser (Jocrisse-Maître et Jocrisse-Valet en particulier m’a amusé), les mettre sur scène (j’achèterai des places) ou bien les utiliser pour vos propres recherches. Peut-être vous voudriez aussi contribuer au dépôt, par corriger des erreurs dans les textes, ajouter de nouveaux textes du catalogue, ou convertir les textes en de nouveaux formats, comme TEI ou Markdown.

En janvier 2018 j’ai créé le bot spectacles_xix sur Twitter. Chaque jour il diffuse les descriptions des pièces qui ont débuté ce jour-là il y a exactement deux cents ans.

N’hésitez pas à utiliser ce corpus dans vos recherches, mais je vous prie de ne pas oublier de me citer, ou même me contacter pour discuter des collaborations éventuelles!

Teaching sign linguistics in introductory classes

Language is not just spoken and written, and even though I’ve been working mostly on spoken languages for the past fifteen years, my understanding of language has been tremendously deepened by my study of sign languages. At the beginning of the semester I always asked my students what languages they had studied and what aspects of language they wanted to know more about, and they were always very interested in sign language. Since they had a professor with training and experience in sign linguistics it seemed natural to spend some time on it in class.

Our primary textbook, by George Yule,contains a decent brief overview of sign languages. The Language Files integrates sign language examples throughout and has a large section on sign phonetics. I added a lecture on the history of sign languages in Europe and North America, largely based on Lane, Hoffmeister and Bahan’s Journey Into the Deaf-World (1996), and other information I had learned over the years.

I also felt it was important for my students to actually observe a sign language being used to communicate and to express feeling, so I found an online video of an MIT lecture by psychologist and master storyteller (and co-author of Journey Into the Deaf-World) Ben Bahan. Bahan’s talk does not focus exclusively on language, but demonstrates the use of American Sign Language well, and the English interpretation is well done.

Studying a video lecture is a prime candidate for “flipped classroom” techniques, but I never got around to trying that. We watched the video in class, but before starting the video I assigned my students a simple observation task: could they find examples of the four phonological subsystems of American Sign Language – lexical signs, fingerspelling, depicting signs and nonmanual gestures?

Some of the students were completely overwhelmed by the task at first, but I made it clear that this was not a graded assignment, only introductory exploration. Other students had had a semester or more of ASL coursework, and the students with less experience were able to learn from them. Bahan, being Ben Bahan, produces many witty, thought-provoking examples of all four subsystems over the course of the lecture.

The phonological subsystems are among the easiest sign language phenomena for a novice to distinguish, but as we watched the video I pointed out other common features of ASL and other sign languages, such as topic-comment structures and stance-shifting.

Later, when I started teaching Introduction to Phonology, we had the opportunity to get deeper into sign language phonology. I’ll cover that in a future post.

Indistinguishable from magic

You might be familiar with Arthur C. Clarke’s Third Law, “Any sufficiently advanced technology is indistinguishable from magic.” Clarke tucked this away in a footnote without explanation, but it fits in with the discussion of magic in Chapter III of James Frazer’s magnum opus The Golden Bough. These two works have shaped a lot of my thoughts about science, technology and the way we interact with our world.

Frazer lays out two broad categories of magic, homeopathic magic and contagious magic. Homeopathic magic follows the Law of Similarity, and involves things like creating effigies of people in order to hurt them, and keeping red birds to cure fever. Contagious magic follows the Law of Contact, and involves things like throwing a child’s umbilical cord into water to improve the child’s swimming abilities later in life, or a young woman planting a marigold into dirt taken from a man’s footprint to help his love for her grow.

Frazer is careful to observe that the Laws of Similarity and Contact are widespread cognitive patterns that people use to understand their environments. In semantics we know them as the foundation of the processes of metaphor and metonymy, respectively. He notes that sympathetic magic’s “fundamental conception is identical with that of modern science: underlying the whole system is a faith, implicit but real and firm, in the order and uniformity of nature.”

In this both science and magic stand in contrast to religion: “if religion involves, first, a belief in superhuman beings who rule the world, and second, an attempt to win their favour, it clearly assumes that the course of nature is to some extent elastic or variable, and that we can persuade or induce the mighty beings who control it to deflect, for our benefit, the current of events from the channel in which they would otherwise flow.” After this Frazer engages in some sloppy thinking, concluding that because religion seems to have arisen after magic it must be an improvement over what the “savages” do. He also fails to complete the fourth quadrant of his taxonomy: that as science is to magic, social sciences are to religion.

The key difference between magic and science (and between religion and social science) is the element of faith. The potion brewer doesn’t check to see that there is a logical explanation for the inclusion of certain ingredients. If the potion fails, she must have gotten impure ingredients, or misread the incantation. A scientist looks for explanations as to why a medicine works when it works, and why it fails when it fails.

Some of you may be thinking that Clarke’s quote was about technology, not science. I first learned of technology as “applied science,” which should mean that it’s no more faith-based than science itself. In practice, it is not possible to understand every tool we use. In fact, it’s not even possible for a human to completely understand a single tool, in all its complexity.

My stepfather was a carpenter. When I was first taught to hammer a nail, I started out by picking the hammer up and putting it down on the nail, vertically. I had to be shown how to swing the hammer to take advantage of the angular momentum of the hammer head. It took another layer of learning to know that I could swing from my wrist, elbow or shoulder to customize the force of the hammer blow to the task at hand, and then another to get a sense of the various types of hammers available, not to mention the various types of nails. In a home improvement project several years ago I discovered that, as electric screwdrivers have gotten smaller and lighter, practices have changed and people use screws in situations where nails used to be more common.

My stepfather might at some point have explained to me why his hammer heads were steel and not iron, and the handles were hardwood and not softwood, metal or fiberglass, but his explanations did not go to the molecular level, much less the atomic or quantum levels. To be honest, all I needed to know was “steel is solid, heavy and doesn’t rust” and “hardwood is solid but absorbs some of the impact.” The chance that the molecular or subatomic structure of the hammers would affect our work beyond that was so small that it wasn’t worth spending time on.

At the beginning I didn’t even need to know that much. All I needed to know was that my stepfather had handed me this hammer and these nails, and told me to nail those two boards together at that angle. I had to trust his expertise. As I began to get comfortable, I started asking him questions and trying things slightly different ways. Eventually people get to the point of saying, “Why not a fiberglass handle?” and even “Why not an electric screwdriver?” But at first it’s 99 percent faith-based.

That’s how the technology of hammers and nails and houses works, but the same principles apply to technologies that many people take for granted, like pencils (we know how to sharpen them, but how many of us know how to mine graphite?) and clothing (some of us can darn a sock, and some of us can knit a scarf, but how many of us have even seen any of the machines that produce shoelaces, or Spanx?). We take it on faith that the pencils will write like they’re supposed to, and that socks will keep our feet warm.

This, then, is what Clarke meant when he talked about technology being indistinguishable from magic. Yes, Sprague de Camp portrayed ancient Romans mistaking explosives for magic in his 1939 novel Lest Darkness Fall (which explicitly invokes the sympathetic and contagious forms of magic described by Frazer). And the magically moving photographs described by J.K. Rowling in Harry Potter and the Philosopher’s Stone have become real technology just twenty years later, omnipresent in the UK and the United States.

But beyond the simple resemblance between technology and magic, if someone is not inclined to be critical or scientific, their relationship to technology is functionally the same as it would be to magic. If the technology is sufficiently advanced, people can do the same things they’ve always done. They don’t need to “get under the hood” (now there’s an example of non-magical technology!) because it seems to work most of the time,

On the other hand, our faith is not blind. I had faith in my stepfather to teach me carpentry because my mother and I had lived with him and trusted him, and seen his work. I also learned to have faith in cars to get me places safely, but as I learned more about kinematics and human attention, and as I was confronted with more evidence of the dangers of this technology, I realized that my faith was misplaced and revised my habits.

Our faith in these technologies is based on a web of trust: I trusted my stepfather when he told me that if I hit the nails this way they would securely fasten the pieces of this house together and if properly maintained, it wouldn’t fall down on us. He in turn trusted his training from other carpenters and recommendations from other professionals in related fields, which were then corroborated, revised and extended by his experiences.

I want to stress here that these methods were also supported by scientific studies of materials and manufacturing. Over the millennia, carpenters, architects and other craftspeople have tried using different materials, different structures, different techniques. Some worked better, some didn’t work so well. They’ve taken the materials apart to figure out what makes them strong in some ways and flexible in other ways. This is an ongoing process: vinyl siding may have seemed like a good idea at the time, but it can pollute if burned or discarded.

That is how you tell the difference between technology and magic: every aspect of the technology is open to question and revision. With magic, you may be able to try new things or test the existing methods, but beyond a certain point there is no more trying or testing, there is only faith.

Why do people make ASL translations of written documents?

My friend Josh was puzzled to see that the City of New York offers videos of some of its documents, translated from the original English into American Sign Language, on YouTube. I didn’t know of a good, short explainer online, and nobody responded when I asked for one on Twitter, so I figured I’d write one up.

The short answer is that ASL and English are completely different language, and knowing one is not that much help learning the other. It’s true that some deaf people are able to lipread, speak and write fluent English, this is generally because they have some combination of residual hearing, talent, privilege and interest in language. Many deaf people need to sign for daily conversation, even if they grew up with hearing parents.

It is incredibly difficult to learn to read and write a language that you can’t speak, hear, sign or see. As part of my training in sign linguistics I spent time with two deaf fifth grade students in an elementary school in Albuquerque. These were bright, curious children, and they spent hours every day practicing reading, writing, speaking and even listening – they both had cochlear implants.

After visiting these kids several times, talking with them in ASL and observing their reading and writing, I realized that at the age of eleven they did not understand how writing is used to communicate. I asked them to simply pass notes to each other, the way that hearing kids did well before fifth grade. They did each write things on paper that made the other laugh, but when I tried giving them specific messages and asking them to pass those messages on in writing, they had no idea what I was asking for.

These kids are in their thirties now, and they may well be able to read and write English fluently. At least one had a college-educated parent who was fluent in both English and ASL, which helps a lot. Other factors that help are the family’s income level and a general skill with languages. Many deaf people have none of these advantages, and consequently never develop much skill with English.

The City could even print some of these documents in ASL. Several writing systems have been created for sign languages, some of them less complete than others. For a variety of reasons, they haven’t caught on in Deaf communities, so using one of those would not help the City get the word out about school closures.

The reasons that the City government provides videos in ASL are thus that ASL is a completely different language from English, many deaf people do not have the exceptional language skills necessary to read a language they don’t really speak, and the vast majority of deaf people don’t read ASL.

On this day in Parisian theater

Since I first encountered The Parisian Stage, I’ve been impressed by the completeness of Beaumont Wicks’s life’s work: from 1950 through 1979 he compiled a list of every play performed in the theaters of Paris between 1800 and 1899. I’ve used it as the basis for my Digital Parisian Stage corpus, currently a one percent sample of the first volume (Wicks 1950), available in full text on GitHub.

Last week I had an idea for another project. Science requires both qualitative and quantitative research, and I’ve admired Neil Freeman’s @everylotnyc Twitter bot as a project that conveys the diversity of the underlying data and invites deep, qualitative exploration.

In 2016, with Timm Dapper, Elber Carneiro and Laura Silver I forked Freeman’s everylotbot code to create @everytreenyc, a random walk through the New York City Parks Department’s 2015 street tree census. Every three hours during normal New York active time, the bot tweets information about a tree from the database, in a template written by Laura that may also include topical, whimsical sayings.

Recently I’ve encountered a lot of anniversaries. A lot of it is connected to the centenary of the First World War I, but some is more random: I just listened to an episode of la Fabrique de l’histoire about François Mitterrand’s letters to his mistress that was promoted with the fact that he was born in 1916, one hundred years before that episode aired, even though he did not start writing those letters until 1962.

There are lots of “On this day” blogs and Twitter feeds, such as the History Channel and the New York Times, and even specialized feeds like @ThisDayInMETAL. There are #OnThisDay and #otd hashtags, and in French #CeJourLà. The “On this day” feeds have two things in common: they tend to be hand-curated, and they jump around from year to year. For April 13, 2014, the @CeJourLa feed tweeted events from 1849, 1997, 1695 and 1941, in that order.

Two weeks ago I was at the Annual Convention of the Modern Language Association, describing my Digital Parisian Stage corpus, and I realized that in the Parisian Stage there were plays being produced exactly two hundred years ago. I thought of the #OnThisDay feeds and @everytreenyc, and realized that I could create a Twitter bot to pull information about plays from the database and tweet them out. A week later, @spectacles_xix sent out its first automated tweet, about the play la Réconciliation par ruse.

@spectacles_xix runs on Pythonanywhere in Python 3.6, and accesses a MySQL database. It uses Mike Verdone’s Twitter API client. The source is open on GitHub.

Unlike other feeds, including this one from the French Ministry of Culture that just tweeted about the anniversary of the première of Rostand’s Cyrano de Bergerac, this one will not be curated, and it will not jump around from year to year. It will tweet every play that premièred in 1818, in order, until the end of the year, and then go on to 1819. If there is a day when no plays premièred, like January 16, @spectacles_xix will not tweet.
I have a couple of ideas about more features to add, so stay tuned!

Remembering Alan Hudson

On Saturday I found out that Alan Hudson died. Alan was my doctoral advisor at the University of New Mexico until his retirement in 2005, and a source of support after that.

I first met Alan when I visited the UNM Linguistics Department in 1997. Alan welcomed me into his office with a broad smile, and asked, “So Angus, have you made up your mind about whether you want to come here?”

“Well…” I said. I had been accepted into the PhD program, but had just come from a very discouraging encounter with another professor, and was ready to give up and go home. Before I could continue, Alan said, “Is there anything I can say to convince you?” I replied, “Well, I guess you just did.”

Alan was not a big name in linguistics; he never published a book. I regularly had to tell people that my advisor was not Dick Hudson. But Alan had a profound insight about the sociology of language that changed my career trajectory and my thinking about language and social justice.

In a seminar on Societal Bilingualism the next year, Alan led us through the case studies laid out by Joshua Fishman, his own advisor, in his book Reversing Language Shift. Fishman’s book is of interest to anyone concerned with language “death” (a problematic metaphor unless the language users themselves are being killed). As a Dubliner who had become fluent in Irish through compulsory government schooling, Alan cared deeply about his national language, but he did not have high hopes for it recovering its status as the primary language of Ireland.

Fishman argues that we can prevent large numbers of people abandoning a language by establishing “diglossia” – arrangements where language H is used for some functions and language L is used for others. Charles Ferguson had shown in 1949 that diglossic arrangements tend to be stable over time. Fishman believed that if language users can establish similar functional separations, they can stop language shift.

Drawing in part on his own research in Ireland and Switzerland, Alan observed that the cases Fishman categorized as diglossia did not fit with Ferguson’s examples. The key factor in Ferguson’s cases was that there were no children in the speech community who are native speakers of H: no child speakers of High German in Switzerland, no child speakers of Metropolitan French in Haiti, etc. In Ireland, by contrast, there are millions of English-speaking children, and in the Netherlands Frisian-speaking children go to school with Dutch-speaking peers.

The result of this contact is that most of these children eventually shift to the higher-prestige, better-paying language, and will not pass their native languages on to their children. There are only two ways to stop it: reverse the power dynamic (as happened in Finland when Russia conquered it from Sweden, I discovered in a term paper that semester) or isolate the children (as Kamal Sridhar observed in her Thanjavur Marathi community).

This was an important insight, with major implications for linguistics. None of us in the course were interested in segregating language groups from each other, and as linguists we were not positioned to shift the socioeconomic power differentials between groups. If the prescription for reversing language shift can be captured in a single sentence, that leaves no ongoing role for linguists.

Since then I have not been terribly surprised that Alan’s insight has not been enthusiastically embraced by other linguists. As Upton Sinclair said, “It is difficult to get a man to understand something, when his salary depends upon his not understanding it!” Alan published two articles describing his definition of diglossia, but framed it in theoretical terms, downplaying the implications for efforts at language maintenance and revitalization.

Alan Hudson supervised my studies and my comprehensive exams, but retired before I was ready to begin my dissertation. He continued to provide valuable advice, and attended my dissertation defense. He will be remembered as an insightful linguist and a supportive teacher.

Data science and data technology

The big buzz over the past few years has been Data Science. Corporations are opening Data Science departments and staffing them with PhDs, and universities have started Data Science programs to sell credentials for these jobs. As a linguist I’m particularly interested in this new field, because it includes research practices that I’ve been using for years, like corpus linguistics and natural language processing.

As a scientist I’m a bit skeptical of this field, because frankly I don’t see much science. Sure, the practitioners have labs and cool gadgets. But I rarely see anyone asking hard questions, doing careful observations, creating theories, formulating hypotheses, testing the hypotheses and examining the results.

The lack of careful observation and skeptical questioning is what really bothers me, because that’s what’s at the core of science. Don’t get me wrong: there are plenty of people in Data Science doing both. But these practices should permeate a field with this name, and they don’t.

If there’s so little science, why do we call it “science”? A glance through some of the uses of the term in the Google Books archive suggests that it was first used in the late twentieth century it did include hypothesis testing. In the early 2000s people began to use it as a synonym for “big data,” and I can understand why. “Big data” was a well-known buzzword associated with Silicon Valley tech hype.

I totally get why people replaced “big data” with “data science.” I’ve spent years doing science (with observations, theories, hypothesis testing, etc.). Occasionally I’ve been paid for doing science or teaching it, but only part time. Even after getting a PhD I had to conclude that science jobs that pay a living wage are scarce and in high demand, and I was probably not going to get one.

It was kind of exciting when I got a job with Scientist in the title. It helped to impress people at parties. At first it felt like a validation of all the time I spent learning how to do science. So I completely understand why people prefer to say they’re doing “data science” instead of “big data.”

The problem with being called a Scientist in that job was that I wasn’t working on experiments. I was just helping people optimize their tools. Those tools could possibly be used for science, but that was not why we were being paid to develop them. We have a word for a practice involving labs and gadgets, without requiring any observation or skepticism. That word is not science, it’s technology.

Technology is perfectly respectable; it’s what I do all day. For many years I’ve been well paid to maintain and expand the technology that sustains banks, lawyers, real estate agents, bakeries and universities. I’m currently building tools that help instructors at Columbia University with things like memorizing the names of their students and sending them emails. It’s okay to do technology. People love it.

If you really want to do science and you’re not one of the lucky ones, you can do what I do: I found a technology job that doesn’t demand all my time. Once in a while they need me to stay late or work on a weekend, but the vast majority of my time outside of 9-5 is mine. I spend a lot of that time taking care of my family and myself, and relaxing with friends. But I have time to do science.

Teaching with accent tags in the face-to-face classroom

In September I wrote about how I used accent tag videos to teach phonetic transcription in my online linguistics classes. Since I could not be there in person, the videos provided a stable reference that we could all refer to from our computers around the country. Having two pronunciations to compare drew the students’ attention to the differences between them – one of the major reasons phonetic transcription was invented – and the most natural level of detail to include in the answer.

In the Fall of 2015 I was back in the classroom teaching Introduction to Phonology, and I realized that those features – a stable reference and multiple pronunciations of the same word with small differences – were also valuable when we were all in the same room. I used accent tag clips in exercises on transcription and other skills, such as identifying phonetic traits like tongue height and frication.

One of my students, Alice Nkanga, pointed out a feature of YouTube that I wasn’t aware of before: you can adjust the speed of playback down to one-quarter speed, and it auto-corrects the pitch, which can help with transcription.

After reading my previous post another linguist, Jessi Grieser, said that she liked the idea, so I shared some of my clips with her. She used them in her class, including a clip I made contrasting two African American women – one from Chicago and one from New York – saying the word “oil.”

Grieser reported, “this went excellently! It really helped hammer home the idea that there isn’t a ‘right’ way to transcribe a word based on its orthography–that what we’re really looking for is a transcription which captures what the speaker did. They really had fun with ‘oil’ since many of them are /AHL/ or /UHL/ speakers themselves. It was a really great discussion starter for our second day of transcription. This is a genius idea.”

It makes me really happy to know that other people find this technique useful in their classrooms, because I was so excited when I came up with it. I would make the clips available to the public, even at no charge, but I’m not sure about the rights because I did not make the original accent tag videos. I hope you’ll all make your own, though – it’s not that hard!

How Google’s Pixel Buds will change the world!

Scene: a quietly bustling bistro in Paris’s 14th Arrondissement.

SERVER: Oui, vous désirez?
PIXELBUDS: Yes, you desire?
TOURIST: Um, yeah, I’ll have the steak frites.
PIXELBUDS: UM, OUAIS, JE VAIS AVOIR LES FRITES DE STEAK
SERVER: Que les frites?
PIXELBUDS: Than fries?
TOURIST: No, at the same time.
PIXELBUDS: NON, EN MEME TEMPS
SERVER: Alors, vous voulez le steak aussi?
PIXELBUDS: DESOLE, JE N’AI PAS COMPRIS.
SERVER: VOUS VOULEZ LE STEAK AUSSI?
PIXELBUDS: You want the steak too?
TOURIST: Yeah, I just ordered the steak.
PIXELBUDS: OUAIS, JE VIENS DE COMMANDER LE STEAK
SERVER: Okay, du steak, et des frites, en même temps.
PIXELBUDS: Okay, steak, and fries at the same time.
TOURIST: You got it.
PIXELBUDS: TU L’AS EU.

(All translations by Google Translate. Photo: Alain Bachelier / Flickr.)