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.)

Teaching phonetic transcription online

When I was teaching introductory linguistics, I had a problem with the phonetic transcription exercises in the textbooks I was using: they asked students to transcribe “the pronunciation” of individual words – implying that there is a single correct pronunciation with a single correct transcription. I worked around it in face-to-face classes by hearing the students’ accents and asking them to pronounce any words if their transcriptions differed from what I expected. I was also able to illustrate the pronunciation of various IPA symbols by pronouncing the sounds in class.

In the summer of 2013 I taught linguistics online for the first time, and it was much more difficult to give students a sense of the sounds I expected them to produce, and to get a sense of the sounds they associated with particular symbols. On top of that I discovered I had another challenge: I couldn’t trust these students to do the work if the answers were available anywhere online. Some of them would google the questions, find the answers, copy and paste. Homework done!

Summer courses move so fast that I wasn’t able to change the exercises until it was too late. In the fall of 2014 I taught the course again, and created several new exercises. I realized that there was now a huge wealth of speech data available online, in the form of streaming and downloadable audio, created for entertainment, education and archives. I chose a podcast episode that seemed relatively interesting and asked my students to transcribe specific words and phrases.

It immediately became clear to me that instead of listening to the sounds and using Richard Ishida’s IPA Picker or another tool to transcribe what they heard, the students were listening to the words, looking them up one by one in the dictionary, and copying and pasting word transcriptions. In some cases Roman Mars’s pronunciations were different from the dictionary transcriptions, but they were close enough that my low grades felt like quibbling to them.

I tried a different strategy: I noticed that another reporter on the podcast, Joel Werner, spoke with an Australian accent, so I asked the students to transcribe his speech. They began to understand: “Professor, do we still have to transcribe the entire word even though a letter from the word may not be pronounced due to an accent?” asked one student. Others noticed that the long vowels were shifted relative to American pronunciations.

For tests and quizzes, I found that I could make excerpts of sound and video files using editing software like Audacity and Microsoft Movie Maker. That allowed me to isolate particular words or groups of words so that the students didn’t waste time locating content in a three-minute video, or a twenty-minute podcast.

This still left a problem: how much detail were the students expected to include, and how could I specify that for them in the instructions? Back in 2013, in a unit on language variation, I had used accent tag videos to replace the hierarchy implied in most discussions of accents with a more explicit, less judgmental contrast between “sounds like me” and “sounds different.” I realized that the accent tags were also good for transcription practice, because they contained multiple pronunciations of words that differed in socially meaningful ways – in fact, the very purpose that phonetic transcription was invented for. Phonetic transcription is a tool for talking about differences in pronunciation.

The following semester, Spring 2015, I created a “Comparing Accents” assignment, where I gave the students links to excerpts of two accent tag videos, containing the word list segment of the accent tag task. I then asked them to find pairs of words that the two speakers pronounced differently and transcribe them in ways that highlighted the differences. To give them practice reading IPA notation, I gave them transcriptions and asked them to upload recordings of themselves pronouncing the transcriptions.

I was pleased to find that I actually could teach phonetic transcription online, and even write tests that assessed the students’ abilities to transcribe, thanks to accent tag videos and the principle that transcription is about communicating differences.

I found these techniques to be useful for teaching other aspects of linguistics. I’ll talk about that in future posts.

Othering, dehumanization and abuse

In a comment, Candy asked:

Can we also please talk about how “cis” is used as a term of abuse against feminists? As in, “shut up privileged cis bitches”? It’s the bit where trans activism begins to overlap with Men Rights Activism.

The use of “shut up” and “bitches” in Candy’s (unattested) example is definitely abuse, but in this dismissive context, “cis” is not functioning as abuse but othering. It positions the referent as an outsider who has no standing in the group, and possibly a threat. Othering can hurt, and it can often be done with malicious intent, but it is not the same as abuse, and responding to it as though it were abuse is generally not effective.

We can distinguish othering from abuse by removing the abusive terms and imagining a different context. Imagine that you have a group of army officers discussing how to attack a fort. Someone with no expertise is walking by and says, “Hey guys, you should just hit the tower with a bazooka!” The officers would be justified in saying, “Are you an army officer? What do you know?” or just “Get this civilian out of here!” “Civilian” isn’t a term of abuse here. It’s othering, but without malicious intent.

Othering is close to dehumanizing, which is a process where categories of people are reframed as enemies unworthy of common decency. This is a well-documented response to trauma, but it can also be done without trauma, when one group is framed as an existential threat to another. This framing can be done quite cynically, as Bosnian Serb leader Radovan Karadži? did with Muslims and later with NATO troops. As I’ve discussed on my trans blog, much of the hatred against gay men, lesbians, trans people, and women who don’t obey men is often in response to a framing that portrays them as unwilling to cooperate in increasing the birth rate of the group. I’m sure any of you can think of several more examples.

Othering is connected with abuse because dehumanizing is an invitation to abuse. If someone is really The Enemy, and unworthy of common decency, then any attacks on them are allowed. Restrictions demarcating acceptable conduct like forum rules and rules against torture are seen as an inconvenience at best, and at worst a dangerous vulnerability at times when “we” can least afford it.

Othering and dehumanizing are forms of category profiling: substituting a category of people for the feature that is required. The army officers have training and experience attacking forts, and in theory they’ve been promoted because they’ve demonstrated some skill. There’s no evidence that this civilian has training, experience or skill. Similarly, German soldiers on the Western Front in World War I were under genuine mortal threat from French and British soldiers who had been ordered to kill them, but there was no evidence that, say, Mexican soldiers were a threat to them at that time.

Of course, category profiling can go wrong in decision-making. There are many examples of experts failing spectacularly, and of outsiders succeeding where the experts don’t. There’s a whole genre of stories about these, like the film Working Girl, where our heroine’s financial expertise is dismissed because she’s categorized as a secretary. When she changes her clothes and hairstyle, people classify her as a financial executive, take her recommendations seriously, and make money.

Profiling has a notorious record in connection with dehumanization. The way that the US treated Russians and Communists when I was a kid, and Arabs and Muslims now, is far out of proportion to any threat. Profiling almost invariably misses some threats, and innocent people are always caught up in the mess.

Now let’s bring in the principle of “nothing for us without us,” which is a foundation of both representative government and identity politics. The idea is that people are experts in the issues that affect them, and the best people to discuss issues affecting a category of people are members of that category. It is very common in activist movements to insist on centering members of a particular category and excluding non-members – either from participating at all, or from taking part in the discussion. This is why othering statements like Candy’s constructed example are common in activist contexts.

When the “nothing for us without us” interacts with dehumanization, it leads to fear that They are pretending to be Us, derailing our discussions within the group and misrepresenting our goals to the wider public. This is not some paranoid fantasy: there is a long history of people infiltrating enemy movements with the goal of spying and disrupting. Recent examples from the FBI include the COINTELPRO infiltration of anti-racist groups in the 1960s and 1970s, and ongoing infiltration of Islamic groups. Our interactions increasingly take place online, where it can be even more difficult to judge people’s affiliations and motives.

Of course, the idea of representation immediately bumps square up against the profiling problem. The fact that someone is affected by an issue is no guarantee that they understand that issue, or have any ability to communicate it or resolve it. It is also no guarantee that they are sane or ethical. It can also be difficult to pin down the specific category affected and center just the members of that category. For example, low-income female-presenting nonwhite transgender people working in sex industries are targets of violence and discrimination, but even if trans people are given voice to talk about it they are often white, relatively affluent, not sex workers and even male-presenting. And because categories can be messy, it is sometimes possible for people to be simultaneously (or intermittently) part of one category that needs representation and another category that some in the first see as a threat.

Several people, including myself and Third Way Trans, have observed that it is common for trans people to have a history of trauma. This leads many trans people to take an us-and-them view of the world, where all trans people are good and innocent, and all “cis” people are evil abusers – despite the fact that trans people are just as likely to be abusive as anyone else. Their trauma leads to othering and dehumanization, and that invites abuse.

So that’s the answer to Candy’s question: “cis” in this context is not used as a term of abuse. It is used for othering, and in combination with the dehumanization that many trans people practice, that justifies the abuse. I don’t see any direct connection to “Men’s Rights Activism.” Thanks for your question, Candy; I hope this helps!

Teaching language variation with accent tag videos

Last January I wrote that the purpose of phonetic transcription is to talk about differences in pronunciation. Last December I introduced accent tags, a fascinating genre of self-produced YouTube videos of crowdsourced dialectology and a great source of data about language variation. I put these together when I was teaching a unit on language variation for the second-semester Survey of Linguistics course at Saint John’s University. When I learned about language variation as an undergraduate, it was exciting to see accents as a legitimate object of study, and it was gratifying to see my family’s accents taken seriously.

At the same time, the focus on a single dialect at a time contrasts with the absence of variation from the discussion of English pronunciation, grammar and lexis in other units, and in the rest of the way English is typically taught. This implies that there is a single standard that does not vary, despite evidence from perceptual dialectology (such as Dennis Preston’s work) that language norms are fragmentary, incomplete and contested. I saw the cumulative effects of this devaluation in class discussions, when students openly denigrated features of the New York accents spoken by their neighbors, their families and often the students themselves.

At first I just wanted to illustrate variation in African American accents, but then I realized that the accent tags allowed me to set up the exercises as an explicit contrast between two varieties. I asked my students to search YouTube to find an accent tag that “sounds like you,” and one that sounded different, and to find differences between the two in pronunciation, vocabulary and grammar. I followed up on this exercise with other ones asking students to compare two accent tags from the same place but with different ethnic, economic or gender backgrounds.

My students did a great job at finding videos that sounded like them. Most of them were from the New York area, and were able to find accent tags made by people from New York City, Long Island or northern New Jersey. Some students were African American or Latin American, and were able to find videos that demonstrated the accents, vocabulary and grammar common among those groups. The rest of the New York students did not have any features that we noticed as ethnic markers, and whether the students were Indian, Irish or Circassian, they were satisfied that the Italian or Jewish speakers in the videos sounded pretty much like them.

Some of the students were from other parts of the country, and found accent tags from California or Boston that illustrated features that the students shared. A student from Zimbabwe who is bilingual in English and Shona was not able to find any accent tags from her country, but she found a video made by a white South African and was able to identify features of English pronunciation, vocabulary and grammar that they shared.

As I wrote last year, the phonetic transcription exercises I had done in introductory linguistics and phonology courses were difficult because they implicitly referred to unspecified standard pronunciations, leading to confusion among the students about the “right” transcriptions. In the variation unit, when I framed the exercise as an explicit comparison between something that “sounds like you” and something different, I removed the implied value judgment and replaced it with a neutral investigation of difference.

I found that this exercise was easier for the students than the standard transcription problems, because it gave them two recordings to compare instead of asking them to compare one recording against their imagination of the “correct” or “neutral” pronunciation. I realized that this could be used for the regular phonetics units as well. I’ll talk about my experiences with that in a future post.

And we mean really every tree!

When Timm, Laura, Elber and I first ran the @everytreenyc Twitter bot almost a year ago, we knew that it wasn’t actually sampling from a list that included every street tree in New York City. The Parks Department’s 2015 Tree Census was a huge undertaking, and was not complete by the time they organized the Trees Count! Data Jam last June. There were large chunks of the city missing, particularly in Southern and Eastern Queens.

The bot software itself was not a bad job for a day’s work, but it was still a hasty patch job on top of Neil Freeman’s original Everylotbot code. I hadn’t updated the readme file to reflect the changed we had made. It was running on a server in the NYU Computer Science Department, which is currently my most precarious affiliation.

On April 28 I received an email from the Parks Department saying that the census was complete, and the final version had been uploaded to the NYC Open Data Portal. It seemed like a good opportunity to upgrade.

Over the past two weeks I’ve downloaded the final tree database, installed everything on Pythonanywhere, streamlined the code, added a function to deal with Pythonanywhere’s limited scheduler, and updated the readme file. People who follow the bot might have noticed a few extra tweets over the past couple of days as I did final testing, but I’ve removed the cron job at NYU, and @everytreenyc is now up and running in its new home, with the full database, a week ahead of its first birthday. Enjoy the dérive!

Online learning and intellectual honesty

In January I wrote that I believe online learning is possible, but I have doubts about whether online courses are an adequate substitute for in-person college classes, let alone an improvement. One of those doubts concerns trust and intellectual honesty.

Any course is an exchange. The students pay money to the college, the instructor gets a cut, and the students get something of value in return. What that something is can be disputed. In theory, the teacher gives the students knowledge: information and skills.

In practice, some of the students actually expect to receive knowledge in exchange for their tuition. Some of them want knowledge but have gotten discouraged. Some wouldn’t mind a little knowledge, but that’s not what they’re there for. Others just have no time for actual learning.

If they’re not there for knowledge, why are they there? For credentials. They want a degree, and the things that go with a degree and make it more valuable for getting a good job: a major, a course list, good grades, letters of recommendation, connections.

If learning is not important, or if the credentials are urgent enough, it is tempting to skip the learning, just going through the motions. That means pretending to learn, or pretending that you learned more than you did. Most teachers have encountered this attitude at some point.

I have seen various manifestations of the impulse to cheat in every class I’ve taught over the years. Some people might be tempted to treat it like any other transaction. It is hard to make a living while being completely ethical. I fought it for several reasons.

First, I genuinely enjoy learning and I love studying languages, and I want to share that enjoyment and passion with my students. Second, many of my students have been speech pathology majors. I have experienced speech pathology that was not informed by linguistics, and I know that a person who doesn’t take linguistics seriously is not fit to be a speech pathologist.

If that wasn’t enough, I was simply not getting paid enough to tolerate cheating. At the wages of an adjunct professor, I wasn’t in it for the money. I was doing it to pass on my knowledge and gain experience, and looking the other way while students cheated was not the kind of experience I signed up for.

I’ve seen varying degrees of dishonesty in my years of teaching. In one French claws, a student tried to hand in an essay in Spanish; in his haste he had chosen the wrong option on the machine translation app. I developed strategies for deterring cheating, such as multiple drafts and a focus on proper citation. But I was not prepared for how much cheating I would find when I taught an online course.

The most effective deterrent was simply to get multiple examples of a student’s work: in class discussions, in small group work, in homeworks and on exams. That allowed me to spot inconsistent quality that might turn out to be plagiarism.

In these introductory linguistics courses, the homeworks themselves were minor exercises, mainly for the students to get feedback on whether they had understood the reading. If a student skipped a reading and plagiarized the homework assignment, it would usually be obvious to both of us when we went over the material in class. That would give the student feedback so that they could change their habits before the first exam.

The first term that I taught this course online, I noticed that some students were getting all the answers right on the homeworks. I was suspicious, but I gave the students the benefit of the doubt. Maybe they had taken linguistics in high school, or read some good books.

Then I noticed that the answers were all the same, and I began to notice quirks of language that didn’t fit my students. One day I saw that the answers were all in an unusual font. I googled one of the quirky phrases and immediately found a file of answers to the questions for that chapter.

I started searching around and found answers to every homework in the textbook. These students were simply googling the questions, copying the answers, and pasting them into Blackboard. They weren’t reading and they weren’t discussing the material. And it showed in their test results. But because this was a summer course, they didn’t have time to recover, and they all got bad grades.

I understood where they were coming from. They needed to knock out this requirement for their degree. They didn’t care about linguistics, or if they did, they didn’t have time for it. They wanted to get the work out of the way for this class and then go to their job or their internship or their other classes. Maybe they wanted to go drinking, but I knew these Speech Pathology students well enough to know that they weren’t typically party animals.

I’ve had jobs where I saw shady practices and just went along with it, but in this case I couldn’t do that, for the reasons I gave above. My compensation for this work wasn’t the meager adjunct pay that was deposited in my checking account every two weeks. It was the knowledge that I had passed on some ideas about language to these students. It was also the ability to say that I had taught linguistics, and even online.

The only solution I had to the problem was to write my own homework questions, ones that could be answered online, but where the appropriate answers couldn’t be found with a simple Google search.

The next term I taught the course online I had to deal with students sharing answers – not collaborating in the groups I had carefully constructed so that the student finishing her degree in another state could learn through peer discussion, but where one student simply copied the homework her friend had done. They did it on exams too, where they were supposed to be answering the questions alone. This meant that I also had to come up with questions where the answers were individual and couldn’t be copied.

I worked hard at it. My student evaluations for the online courses were pretty bad for that first summer, and for the next term, and the one after that. But the term after that they were almost as good as the ones for my in-person courses.

Unfortunately, that’s when I had to tell my coordinator that I couldn’t teach any more online courses. Because to teach them right required a lot of time – especially if every assignment has to be protected against students googling the answers or shouting them to each other across the room.

The good news is that in this whole process I learned a ton of interesting things about language and linguistics, and how to teach them. I’ve found that many of the strategies I developed for online teaching are helpful for in-person classes. I’m planning to post about some of them in the near future.

The Photo Roster, a web app for Columbia University faculty

Since July 2016 I have been working as Associate Application Systems in the Teaching and Learning Applications group at Columbia University. I have developed several apps, including this Photo Roster, an LTI plugin to the Canvas Learning Management System.

The back end of the Photo Roster is written in Python and Flask. The front end uses Javascript with jQuery to filter the student listings and photos, and to create a flash card app to help instructors learn their students’ names.

This is the third generation of the Photo Roster tool at Columbia. The first generation, for the Prometheus LMS, was famously scraped by Mark Zuckerberg when he extended Facebook to Columbia. To prevent future release of private student information, this version uses SAML and OAuth2 to authenticate users and securely retrieve student information from the Canvas API, and Oracle SQL to store and retrieve the photo authorizations.

It would be a release of private student information if I showed you the Roster live, so I created a demo class with famous Columbia alumni, and used a screen recorder to make this demo video. Enjoy!

I just have to outrun your theory

The Problem

You’ve probably heard the joke about the two people camping in the woods who encounter a hungry predator. One person stops to put on running shoes. The other says, “Why are you wasting time? Even with running shoes you’re not going to outrun that animal!” The other replies, “I don’t have to outrun the animal, I just have to outrun you.”

For me this joke highlights a problem with the way some people argue about climate change. First of all, spreading uncertainty and doubt against competitors is a common marketing tactic, and as Naomi Orestes and Erik Conway documented in their book Merchants of Doubt, that same tactic has been used by marketers against concerns about smoking, DDT, acid rain and most recently climate change.

In the case of climate change, as with fundamentalist criticisms of evolution, there is a lot of stress on the idea that the climatic models are “only a theory,” and that they leave room for the possibility of error. The whole idea is to deter a certain number of influential people from taking action.

That Bret Stephens Column

The latest example is Bret Stephens, newly hired as an opinion columnist by New York Times editors who should really have known better. Stephens’s first column is actually fine on the surface, as far as it goes, aside from some factual errors: never trust anyone who claims to be 100% certain about anything. Most people know this, so if you claim to be 100% certain, you may wind up alienating some potential allies. And he doesn’t go beyond that; I re-read it several times in case I missed anything.

Since all Stephens did was to say those two things, none of which amount to an actual critique of climate change or an argument that we should not act, the intensely negative reactions it generated may be a little surprising. But it helps if you look back at Stephens’s history and see that he’s written more or less the same thing over and over again, at the Wall Street Journal and other places.

Many of the responses to Stephens’s column have pointed out that if there’s any serious chance of climate change having the effects that have been predicted, we should do something about it. The logical next step is talking about possible actions. Stephens hasn’t talked about any possible actions in over fifteen years, which is pretty solid evidence of concern trolling: he pretends to be offering constructive criticism while having no interest in actually doing anything constructive. And if you go all the way back to a 2002 column in the Jerusalem Post, you can see that he was much more overtly critical in the past.

Stephens is very careful not to recommend any particular course of action, but sometimes he hints at the potential costs of following recommendations based on the most widely accepted climate change models. Underlying all his columns is the implication that the status quo is just fine: Stephens doesn’t want to do anything to reduce carbon emissions. He wants us to keep mining coal, pumping oil and driving everywhere in single-occupant vehicles.

People are correctly discerning Stephens’s intent: to spread confusion and doubt, disrupting the consensus on climate change and providing cover for greedy polluters and ideologues of happy motoring. But they play into his trap, responding in ways that look repressive, inflexible and intolerant. In other words, Bret Stephens is the Milo Yiannopoulos of climate change.

The weak point of mainstream science

Stephens’s trolling is particularly effective because he exploits a weakness in the way mainstream scientists handle theories. In science, hypotheses are predictions that can be tested and found to be true or false: the hypothesis that you can sail around the world was confirmed when Juan Sebastián Elcano completed Magellan’s expedition.

Many people view scientific theories as similarly either true or false. Those that are true – complete and consistent models of reality – are valid and useful, but those that are false are worthless. For them, Galileo’s measurements of the movements of the planets demonstrated that the heliocentric model of the solar system is true and the model with the earth at the center is false.

In this all-or-nothing view of science, uncertainty is death. If there is any doubt about a theory, it has not been completely proven, and is therefore worthless for predicting the future and guiding us as we decide what to do.

Trolls like Bret Stephens and the Marshall Institute exploit this intolerance of uncertainty by playing up any shred of doubt about climate change. And there are many such doubts, because this is actually the way science is supposed to work: highlighting uncertainty and being cautious about results. Many people respond to them in the most unscientific ways, by downplaying doubts and pointing to the widespread belief in climate change among scientists.

The all-or-nothing approach to theories is actually a betrayal of the scientific method. The caution built into the gathering of scientific evidence was not intended as a recipe for paralysis or preparation for popularity contests. There is a way to use cautious reports and uncertain models as the basis for decisive action.

The instrumental approach

This approach to science is called instrumentalism, and its core principles are simple: theories are never true or false. Instead, they are tools for understanding and prediction. A tool may be more effective than another tool for a specific purpose, but it is not better in any absolute sense.

In an instrumentalist view, when we find fossils that are intermediate between species it does not demonstrate that evolution is true and creation is false. Instead, it demonstrates that evolution is a better predictor of what we will find underground, and produces more satisfying explanations of fossils.

Note that when we evaluate theories from an instrumental perspective, it is always relative to other theories that might also be useful for understanding and predicting the same data. Like the two people running from the wild animal, we are not comparing theories against some absolute standard of truth, but against each other.

In climate change, instrumentalism simply says that certain climate models have been better than others at predicting the rising temperature readings and melting glaciers we have seen recently. These models suggest that it is all the driving we’re doing and the dirty power plants we’re running that are causing these rising temperatures, and to reduce the dangers from rising temperatures we need to reconfigure our way of living around walking and reducing our power consumption.

Evaluating theories relative to each other in this way takes all the bite out of Bret Stephens’s favorite weapon. He never makes it explicit, but he does have a theory: that we’re not doing much to raise the temperature of the planet. If we make his theory explicit and evaluate it against the best climate change models, it sucks. It makes no sense of the melting glaciers and rising tides, and has done a horrible job of predicting climate readings.

We can fight against Bret Stephens and his fellow merchants of doubt. But in order to do that, we need to set aside our greatest weakness: the belief that theories can be true, and must be proven true to be the basis for action. We don’t have to outrun Stephens’s uncertainty; we just have to outrun his love of the status quo. And instrumentalism is the pair of running shoes we need to do that.