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Speaker 1: Pushkin. I want to start this episode by telling you

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Speaker 1: just the very beginning of a story I recently heard about.

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Speaker 1: A guy named Alex Cogan born in nineteen eighty six

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Speaker 1: into a Jewish family in the Soviet Union. After the

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Speaker 1: collapse in nineteen ninety one, the government loses control and

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Speaker 1: Jews are even less safe than before. Alex's dad starts

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Speaker 1: getting death threats, so he up and moves his entire

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Speaker 1: family four generations of Cogan's, to New York City. In

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Speaker 1: nineteen ninety four, Alex enter's first grade in a Brooklyn

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Speaker 1: public school. He's conspicuous, way taller than the other kids.

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Speaker 1: He speaks no English. He's also got a talent from

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Speaker 1: math and science. Once his teachers can understand him, they

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Speaker 1: think he has the makings of a gifted physicist. Life's

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Speaker 1: not hard for him, but as he grows up, he

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Speaker 1: begins to see that it isn't always easy for everybody else.

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Speaker 1: Six months after they've arrived in the United States, his

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Speaker 1: great grandmother had jumped from their apartment window to her death.

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Speaker 1: His parents, the loves of each other's lives, split up.

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Speaker 1: Alex cries every night until they get back together. He

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Speaker 1: enters high school and one of his close friends attempts suicide,

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Speaker 1: another becomes clinically depressed. Alex begins to read psychology. He's

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Speaker 1: a math and science kid, but he's getting more and

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Speaker 1: more curious about human nature. And the first time I

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Speaker 1: met him, and I really remember it very distinctly, because

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Speaker 1: he almost always wore these giant basketball shorts no matter

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Speaker 1: what the weather. You know, he's terribly dressed, like a

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Speaker 1: lot of Berkeley undergrads, and you know, and basketball shoes.

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Speaker 1: That's Daker Keltner, the psychologist at the University of California,

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Speaker 1: Berkeley who runs something called the Greater Good Science Center

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Speaker 1: where they study human emotion. We heard from him in

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Speaker 1: episode one. Alex Cogan was a shambolic six foot four

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Speaker 1: inch freshman back in two thousand and five when he

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Speaker 1: knocked on Daker's office door and said he'd like for

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Speaker 1: Daker to teach him. Emotions fascinated him. He'd come to

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Speaker 1: cal to study physics, but he'd been thinking about love,

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Speaker 1: about the distinction between loving and being loved. He wanted

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Speaker 1: to study it the way you'd study a quark. And

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Speaker 1: Alex came in and he said, you know, I have

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Speaker 1: seven kinds of love. That I'm going to put people into.

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Speaker 1: I was like, wow, that's interesting. And then there are

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Speaker 1: twelve variations of I forgot what the other factor was

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Speaker 1: that or set of conditions that he wanted to create,

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Speaker 1: And there are eighty four different conditions in his study.

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Speaker 1: So he's going to study seven different kinds of love,

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Speaker 1: and he's going to study all these different variables that

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Speaker 1: would maybe predict the force of the love, the power

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Speaker 1: of the love exactly. So he's about to make glove

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Speaker 1: more complicated than it's ever been made. So it sounds like, right,

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Speaker 1: he was gonna confound our understanding of love. Daker talks

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Speaker 1: Alex out of that idea, but this kid is so

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Speaker 1: smart and original and full of energy, and so Daker

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Speaker 1: takes him in and it isn't long before Alex is

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Speaker 1: finding things to do that no one else is doing.

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Speaker 1: For instance, the thing that he does after they discover

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Speaker 1: a gene it's associated with human kindness. And Alex did

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Speaker 1: this cool paper where he showed if you present videotapes

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Speaker 1: of people who have that gene or this variant of

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Speaker 1: a gene that makes them kind and I am an

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Speaker 1: observer and I see one of those people for twenty

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Speaker 1: seconds on video. I trust them, right, I'm like this guy.

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Speaker 1: I go to battle at this guy, right, I trust

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Speaker 1: this guy. By the time Alex graduates from cal he's

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Speaker 1: established himself as the most promising student in the entire

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Speaker 1: psychology department and the most unusual. Just this big, sweet

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Speaker 1: natured guy with a serious talent for math and statistics

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Speaker 1: and a desire to study huge questions like what is love?

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Speaker 1: When he left and he's so unconventional, Michael, he could

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Speaker 1: have gone to any graduate program in the country, and

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Speaker 1: he chooses the University of Hong Kong. I'm what because

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Speaker 1: he met this woman or got engaged and fell in love? Yeah,

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Speaker 1: fell in love. But Daker and Alex stay in touch.

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Speaker 1: They collaborate on a few papers. They're both interested in

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Speaker 1: big questions about human nature. At the same time, social

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Speaker 1: media has started to create a new way to study

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Speaker 1: those questions. In late twenty twelve, Facebook invites Daker to

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Speaker 1: visit and asks him to create a bunch of new emojis,

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Speaker 1: ones that better convey actual emotions. When Daker sees what

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Speaker 1: Facebook knows about its users, he's blown away. This could

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Speaker 1: be the greatest data source that will ever EXI and

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Speaker 1: it would help us answer questions from the scientific perspective,

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Speaker 1: like how does disease spread in some neighborhoods but not others?

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Speaker 1: What predicts heart attacks? Where does hate crime? Where is

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Speaker 1: it likely to happen? Right? That was all tractable with

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Speaker 1: the data that they had. Meanwhile, Alex had moved to

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Speaker 1: England to teach at Cambridge University. He was still researching

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Speaker 1: the same stuff, the positive emotions, and he too was

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Speaker 1: seeing possibilities in the new social media data. And I

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Speaker 1: was at Facebook doing my consulting work and I saw

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Speaker 1: Alex there. I was like, what are you doing here?

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Speaker 1: And he's everywhere, you know, So he's like, oh, I'm

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Speaker 1: working on this other project and he told me about it.

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Speaker 1: Alex Cogan told Daker that he wanted to use Facebook

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Speaker 1: to study things like love and happiness. For example, you

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Speaker 1: might be able to take a fairly small sample of data,

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Speaker 1: say the likes of ten thousand Facebook users, to make

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Speaker 1: discoveries about those emotions entire countries. The math was complicated

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Speaker 1: enough the Dacker himself didn't fully understand it. He then

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Speaker 1: forgot all about it until one day a year or

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Speaker 1: so later, when Alex Cogan called him up. He calls

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Speaker 1: me after Trump's elected and he says, I think I've

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Speaker 1: done something that was part of this election. And I

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Speaker 1: was like, okay, well, let's talk what is it? And

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Speaker 1: he said, I created this mechanism that was purchased and

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Speaker 1: used in the Trump campaign. He was worried that he

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Speaker 1: actually had had some effect, or that he'd be perceived

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Speaker 1: to have had some effect. I don't think he made

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Speaker 1: that distinction. I just think he thought, oh now, Alex

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Speaker 1: Cogan sensed that he might have a problem. He just

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Speaker 1: had no idea how big it was going to be.

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Speaker 1: I'm Michael Lewis, and this is Against the Rules, a

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Speaker 1: show about the decline of the human referee in American

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Speaker 1: life and what that's doing to our idea of fairness today.

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Speaker 1: I want to talk about an entire species of refs,

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Speaker 1: one that's nearing extinction, whom no one will miss until

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Speaker 1: it's too late. I used to be a referee in

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Speaker 1: the big leagues of dictionaries. The American Heritage. You've heard

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Speaker 1: of it. The American Heritage has something called the usage Panel,

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Speaker 1: and I was on it, along with a couple of

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Speaker 1: hundred other word people. Every year we get this mass

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Speaker 1: email asking us to judge the latest word controversies how

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Speaker 1: certain words should be defined, or spelled or pronounced. English

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Speaker 1: is always changing, and the dictionary wanted to keep up

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Speaker 1: with the times and sometimes resist them. Was it okay

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Speaker 1: to use unique to mean unusual? Should you say banal

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Speaker 1: or banal or both? This year I got a different

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Speaker 1: sort of email, saying I've been fired. They fired the

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Speaker 1: whole panel, so I didn't take it personally, but I

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Speaker 1: still want to know why. As far as I could see,

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Speaker 1: we've done nothing wrong. Our definitions were still definitive. I

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Speaker 1: call the guy who'd been my boss as head of

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Speaker 1: the usage panel. What did you do? I advised on

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Speaker 1: people to include on the usage panel. Occasionally people die,

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Speaker 1: and or occasionally people would simply not respond to the

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Speaker 1: questionnaire for several years running, and we'd want to replace them.

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Speaker 1: His name is Stephen Pinker. Yes, that's Stephen Pinker, Harvard

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Speaker 1: psychologist and author of many best selling books. In the

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Speaker 1: case of disputed usage, where people wonder what is the

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Speaker 1: correct use? Can I use decimate to mean destroy most of, or,

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Speaker 1: as rumor has it should only mean destroy one tenth of?

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Speaker 1: Or what's the best way to use epicenter. Is it

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Speaker 1: just the center of something or does it have to

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Speaker 1: mean propagating outward? Of course, if you want to know

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Speaker 1: what a center means, you can now just google it.

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Speaker 1: The Internet has been bad for dictionaries. They don't sell

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Speaker 1: the way they used to. But the Internet doesn't explain

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Speaker 1: why our panel was fired. We didn't cost the dictionary

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Speaker 1: a dime. We all work for free. Why did they

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Speaker 1: cut it? You know, I haven't gotten to the bottom

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Speaker 1: of this. Maybe I'll just let someone else chase this

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Speaker 1: one down. I mentioned this whole situation because it's not unique, which,

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Speaker 1: by the way, should only be used to mean one

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Speaker 1: of a kind. Nothing can be very unique, or most unique,

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Speaker 1: or even rather unique. A thing can be either banal

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Speaker 1: or banal. But it's either unique or it's not anyway.

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Speaker 1: The death of the word referee is not even all

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Speaker 1: that unusual. They're a member of the species of refs

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Speaker 1: that the world now has no use for. The culture refs,

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Speaker 1: the people who referee are most basic interactions how we

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Speaker 1: should talk, who we should trust, or whom we should trust.

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Speaker 1: No one particularly mourns their death until they really need one.

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Speaker 1: Our bags. We are in a suburb of Dallas, at

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Speaker 1: the home of Brian Garner, who has set himself up

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Speaker 1: as a referee of the English language. When and what

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Speaker 1: should you hyphenate? He's the author of M. Garner's Modern

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Speaker 1: English Usage. Why people shouldn't use flaunt when they mean flout.

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Speaker 1: We've been standing out of here for three or four

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Speaker 1: minutes and there's no sign of life. We're gonna go

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Speaker 1: knock on his door. All right, all right? What's the

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Speaker 1: difference between species and spurious? Does it really matter if you,

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Speaker 1: at this very moment are filled with angst or angst?

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Speaker 1: We're a weird g. Garner's Usage manual is now more

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Speaker 1: than twelve hundred pages long. The late novelist David Foster

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Speaker 1: Wallace called it a work of genius. This book is

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Speaker 1: so big. Did you bring your copy? No? I have

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Speaker 1: xerox those pages that I want, just the front. Yeah,

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Speaker 1: it wouldn't fit. You don't really expect to find guardians

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Speaker 1: of the English language in Dallas, Texas. Then again, you

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Speaker 1: don't really expect to find them anywhere. That's why I've

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Speaker 1: bothered to find him. It's like flying to Indonesia to

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Speaker 1: see the last of the Sumatran rhinos, and so here

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Speaker 1: We are between a giant golf course of a lawn

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Speaker 1: and a monticello of red bricks and doric columns. We're

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Speaker 1: prank in the right place, Michael Lewis, Ryan Garner, very

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Speaker 1: good to meet you. Thank you for letting us in truth.

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Speaker 1: Are we welcome? Garner's house does have a kitchen and bathrooms,

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Speaker 1: almost like a normal house, but it feels like an

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Speaker 1: excuse for him to live in what amounts to a

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Speaker 1: massive library. Floors of books with little ladders so you

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Speaker 1: can climb up and reach them. Thousands upon thousands of

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Speaker 1: mostly very old books about the English language. I've had

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Speaker 1: my coffee all round. It looks like a Robber Baron's

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Speaker 1: collection of books, except they look like they've been read.

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Speaker 1: They look like they aren't. They aren't book spot by

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Speaker 1: the yard, and they also have plastic covers on them,

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Speaker 1: which is a little unusual. How many Usage Experts books

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Speaker 1: do you have in this library? I mean, how many different?

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Speaker 1: He published his first Usage Guide back in nineteen ninety eight,

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Speaker 1: partly as a protest against the way people talked on TV,

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Speaker 1: which sounds a bit snooty, but Gardner's genius was not

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Speaker 1: to set himself up as some kind of elite speaking

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Speaker 1: down to the illiterate masses. His judgments felt like common sense.

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Speaker 1: They were relied on data. He classified any change in

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Speaker 1: the language into five stages, ranging from weird new usage

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Speaker 1: to a totally accepted new use of the word. He

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Speaker 1: had lots of information on how people were actually speaking

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Speaker 1: and writing the English language. So this is Webster's first

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Speaker 1: dictionary six and this just kind of shows the evolution

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Speaker 1: over the nineteenth century. But I have so upstairs. These

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Speaker 1: are books on writing, a beginning all the way over here,

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Speaker 1: so that this whole, that whole wall is linguistics, and

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Speaker 1: look on usage and writing. I think I just assume

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Speaker 1: that anybody who went this far out of his way

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Speaker 1: to tell other people how to speak and write must

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Speaker 1: have something wrong with him. That if you tracked his

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Speaker 1: interest back to its source, you'd finally arrive at the

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Speaker 1: desire to feel superior. But that's not Garner. His source

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Speaker 1: energy isn't snobbery. It's outrage at an idea cooked up

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Speaker 1: by academic linguistics, an idea he had encountered back as

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Speaker 1: a student at the University of Texas descriptivism. It was

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Speaker 1: called a native speaker of English cannot make a mistake,

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Speaker 1: and it's so fact though if a native speaker says it,

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Speaker 1: it is correct. That is a very extreme position to take,

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Speaker 1: and I think an indefensible one, and one that I

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Speaker 1: have pretty much set my face against. He set his

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Speaker 1: face against descriptivism, and his face is set against it. Still.

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Speaker 1: Do you consider yourself a referee? Yes? Yeah, I'm making

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Speaker 1: judgment calls about and there is a lot of judgment involved,

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Speaker 1: But I'm trying to be a helpful guide to writers

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Speaker 1: and speakers of English. We're now up in a balcony

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Speaker 1: gazing down at an amphitheater of books about the English language.

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Speaker 1: He's got a whole other collection of books out back

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Speaker 1: where the poolhouse should be, in a building that's an

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Speaker 1: exact replica of the room in England in which the

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Speaker 1: Oxford English Dictionary was created. I pulled down an especially

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Speaker 1: decrepit looking book by someone I've never heard of, Lindley Murray. Murray,

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Speaker 1: but there's kind of a hero of mine. Interesting guy.

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Speaker 1: He was a New York lawyer in seventeen eighty four.

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Speaker 1: He moved to York, England because he didn't like the Revolution,

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Speaker 1: and a lot of Americans actually moved to England because

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Speaker 1: they didn't appreciate what was going on. Lynn Manuel and

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Speaker 1: Miranda left that out of Hamilton. I guess so, and

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Speaker 1: so these two shelves are whole various ambitions of Murray's Grammar. Yeah,

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Speaker 1: and Brian Garner seems to have all of them. So Murray.

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Speaker 1: In seventeen ninety five he stopped practicing law and he

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Speaker 1: wrote Murray's English Grammar for a Quaker girls school in York,

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Speaker 1: and it became the best selling book in the English

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Speaker 1: language other than the Bible for the first fifty years

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Speaker 1: of the nineteenth century. He sold over thirteen million copies

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Speaker 1: of his English Grammar. Every household needed an English Grammar

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Speaker 1: and a Bible thirteen million copies. The joint population of

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Speaker 1: Great Britain in the United States in eighteen hundred was

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Speaker 1: only fifteen million, But back then people threw money at

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Speaker 1: language refs. Noah Webster got rich from his dictionary, so

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Speaker 1: did Fowler and Follet and Partridge and scores of others

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Speaker 1: from their grammars and usage guides. Strunk and White have

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Speaker 1: sold ten million copies of this style manual. There was

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Speaker 1: a time not long ago when a writer could get

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Speaker 1: paid to write about how to write, and the American

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Speaker 1: Heritage Dictionary used to brag about his usage panel. But

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Speaker 1: Brian Garner is in the wrong century. How many copies

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Speaker 1: of Garner's Modern English usage is sold, I don't know exactly,

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Speaker 1: but it's fewer that hauled and paltry. Brian Garner has

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Speaker 1: a really nice house, but his usage manual doesn't pay

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Speaker 1: his mortgage. He gives writing seminars for lawyers. The rest

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Speaker 1: of his market has mostly vanished. I mentioned Barnes and Noble,

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Speaker 1: but I haven't singled anybody out in particular, although I

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Speaker 1: kind of did when the first two editions of my

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Speaker 1: Usage book came out. Usage Book has passed a we're

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Speaker 1: not going to stalk it. I mean that has a

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Speaker 1: major effect, and they said, no, we've made the decision

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Speaker 1: that really this category is defunct. The usage book is

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Speaker 1: a defunct category. I grab another one of his old

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Speaker 1: books and flip through it. Some nineteenth century guide to pronunciation.

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Speaker 1: The idea that anyone would write, much less pay money

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Speaker 1: for a pronunciation guide, well, it's preposterous and preposterous. It

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Speaker 1: is an interesting fact, and one not sufficiently realized that

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Speaker 1: a person who has a pronunciation of his own for

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Speaker 1: a word is very apt to take it for granted

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Speaker 1: that he hears all others has pronounced it in the

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Speaker 1: same manner, when in fact his own method is entirely

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Speaker 1: peculiar to himself. It doesn't make true at Also, talking

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Speaker 1: about making people incredibly uncomfortable, fearful of what was coming

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Speaker 1: out of their mouths, that's what he's doing. People used

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Speaker 1: to feel uneasy about how they use the language. They

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Speaker 1: didn't want to sound stupid or uneducated. Now they feel

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Speaker 1: uneasy about anyone who would presume to judge how they're

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Speaker 1: using the language, and old anxiety has been replaced by

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Speaker 1: something else, a suspicion of the individual ref People still

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Speaker 1: judge other people by what they say and how they

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Speaker 1: say it, but they do it differently, without reference to

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Speaker 1: a higher authority, but to the crowd. My own bank

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Speaker 1: here in Dallas, every time there would be in any

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Speaker 1: activity on one of my accounts, I'd get an email

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Speaker 1: message dear dear mister Garner semicohen And I called my

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Speaker 1: banker and I said, by the way, you know, you

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Speaker 1: got hundreds of these things, presumably thousands going out by

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Speaker 1: the day, dear customer, semicolon, And I said, you know,

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Speaker 1: it's got to be either comma or a colon. He said,

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Speaker 1: could you put that in writing? And I said, or

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Speaker 1: I'll even give you some authorities, and I cited Garner's

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Speaker 1: Modern English usage and a couple of other authorities on

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Speaker 1: this point of punctuation. It's a pretty elementary point. Yep,

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Speaker 1: he did that. I mean, who else is there to site?

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Speaker 1: But the incorrectly punctuated letters just kept coming. Still. I

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Speaker 1: was getting dozens every week of dear mister Garner semicolon

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Speaker 1: and and it was I was about to change banks

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Speaker 1: over this, because it's it's it's a little upsetting to

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Speaker 1: think I'm doing business with people who are doing something

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Speaker 1: so egregiously bad. And they didn't change it for about

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Speaker 1: a month, and so I called him and I said,

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Speaker 1: what's going on? He said, well, you know, I showed

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Speaker 1: it to some of the people here at the bank,

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Speaker 1: but we have a dispute about whether it should be

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Speaker 1: a semicolon or a colon, and so we just left it.

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Speaker 1: But that that is a demotic view. Well, your your

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Speaker 1: opinion is as good as mine. Anybody's opinion is as

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Speaker 1: good as somebody else. Demotic. Now, there is a word

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Speaker 1: derived from an ancient Greek word meaning popular. That's how

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Speaker 1: the language is generally refereed by popular opinion. Inside Garner's bank,

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Speaker 1: by popular opinion, it was okay to send out letters

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Speaker 1: teeming with semicolons that didn't belong. It's obviously not that

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Speaker 1: big a deal. I mean, you can still understand where

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Speaker 1: the bank was trying to say. Plus, it's sort of

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Speaker 1: freeing to rid ourselves of this expert language ref this

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Speaker 1: annoying little schoolmarmie voice in your head. On the other hand,

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Speaker 1: what happens when that little voice ceases to exist? And

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Speaker 1: not just that little voice, but the other little voices

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Speaker 1: like it. I'm Margaret Sullivan and I was the public

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Speaker 1: editor of the New York Times. And what's a public editor?

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Speaker 1: I just asked that to loosen her up. I knew

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Speaker 1: the answer. The public editor is the ombudsman, the neutral

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Speaker 1: party inside the news organization whose job is to make

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Speaker 1: judgments about the news in the same possibly irritating way

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Speaker 1: that Brian Garner makes judgments about the language, to call

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Speaker 1: out the paper when it screws up. Sullivan did that

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Speaker 1: at the New York Times from two thousand and twelve

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Speaker 1: until the spring of two thousand and sixteen. When she

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Speaker 1: left a year later, the Times just got rid of

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Speaker 1: its public editor altogether. So I would love for you

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Speaker 1: to explain to me the importance of ombudsman why they

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Speaker 1: exist in the first place. So, for example, and this

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Speaker 1: is not the only role, but let's just say someone

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Speaker 1: thinks a correction should be made in a news story

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Speaker 1: and the people who are in charge of that say, well, nope,

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Speaker 1: we're not going to do that because we're convinced it's right.

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Speaker 1: So then they could come to the ombudsman and say,

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Speaker 1: what do you think here? The thing about the is

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Speaker 1: that it has to be independent. I had no editor.

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Speaker 1: I mean I had a copy editor, and I end

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Speaker 1: the My copy editor great person would say to me,

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Speaker 1: are you sure you want to say it that way?

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Speaker 1: Or don't you think going a little too far there?

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Speaker 1: But he couldn't tell me not to do it. Sullivan

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Speaker 1: was not just a good ombudsman. She was a famously

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Speaker 1: good one. She made a big deal about reporters who

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Speaker 1: let sources approve their quotes. She called out The Times

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Speaker 1: for its policies allowing anonymous sources, especially in stories about

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Speaker 1: national politics. Everyone in the news room read and feared her,

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Speaker 1: and that probably prevented a lot of distorted or unfair

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Speaker 1: stuff from ever getting into print. But the role she

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Speaker 1: played is dying. The Washington Post got rid of their

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Speaker 1: ombudsman in two and thirteen, and the New York Times

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Speaker 1: in twenty seventeen. Even ESPN had one and got rid

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Speaker 1: of it. And why so, why has it been in decline?

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Speaker 1: If you ask the media organizations, the news organizations who

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Speaker 1: have discontinued their ombudsperson rolls, they would say, almost to

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Speaker 1: a person, they would say, it's not necessary anymore because

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Speaker 1: there's so much criticism in the digital world on Twitter

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Speaker 1: and elsewhere. There's so many voices, there's so many ways

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Speaker 1: to get a complaint or a point of view out

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Speaker 1: there that we don't need to have someone that we

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Speaker 1: pay to criticize us. Internally, you don't need a news

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Speaker 1: reff anymore because in the new media market, the crowd

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Speaker 1: can do the reffing. The Times only created the ombudsman

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Speaker 1: roll back in two thousand and three. The reasoning then

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Speaker 1: was the modern media market, the Internet, cable TV, the

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Speaker 1: speeding up in the news cycle that was all creating

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Speaker 1: pressures that led to some really sensational screw ups by

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Speaker 1: the New York Times. They printed a bunch of stories

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Speaker 1: on the front page by a reporter named Jason Blair.

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Speaker 1: He later confessed that he just made up quotes an

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Speaker 1: entire scenes. They printed stories saying that Sodom Hussein possessed

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Speaker 1: weapons of mass destruction when he didn't. Did you while

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Speaker 1: you were there? Was there? Did you have a sense

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Speaker 1: that there was a decline in the need for you

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Speaker 1: to do this job? Was there? Where? Were there like

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Speaker 1: less things coming in? Oh? No more if anything? But

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Speaker 1: there was this belief in the air that the crowd

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Speaker 1: could do the job. And why pay a genuinely independent

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Speaker 1: news referee when you could get the crowd to do

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Speaker 1: the job for free? Do you ever read it? Does

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Speaker 1: any anything ever cause a story to smell for you?

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Speaker 1: You go, there's something wrong. It's the kind of thing

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Speaker 1: that if I were there in my job, I'd be

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Speaker 1: getting emails about Oh, yes, absolutely, you can see those

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Speaker 1: coming a mile away. Now I'm going to finish the

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Speaker 1: story of Alex Cogan, the young psychologist born in the

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Speaker 1: Soviet Union who started out in physics and ended up

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Speaker 1: in love along with a bunch of other researchers and

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Speaker 1: app builders. He'd signed an agreement with Facebook to study

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00:25:16,356 --> 00:25:20,916
Speaker 1: its users. It wasn't cheap to do. Alex paid the

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Speaker 1: subjects of his studies through some survey company. He asked

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Speaker 1: permission to let him study overall patterns of what they

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Speaker 1: liked and how they used emojis. He hoped that the

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00:25:31,916 --> 00:25:34,916
Speaker 1: data might yield all kinds of insights or help address

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Speaker 1: the odd questions that Alex had a talent for raising,

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00:25:38,036 --> 00:25:41,836
Speaker 1: like what is the difference between loving and being loved?

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Speaker 1: Fast forward to i'd say winter of twenty fourteen, and

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Speaker 1: one of the PhD students in my department at Cambridge says, Hey,

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Speaker 1: I've been consulting for this company. They'd really love to

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Speaker 1: meet you and get like a little consulting help from you.

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Speaker 1: Would you be interesting? I'm like, sure, meet Alex Cogan,

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Speaker 1: student of Love. The big cary for me here was

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Speaker 1: that they were going to pay for a really big

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Speaker 1: data collection. So they're going to pay something like eight

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Speaker 1: hundred thousand dollars so we could get all this data

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Speaker 1: and I could keep it to do my research. And

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00:26:17,276 --> 00:26:20,076
Speaker 1: that was really exciting to me because hey, this was

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Speaker 1: a really fast way to get a really nice grant.

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Speaker 1: So I set up a meeting with this company called

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00:26:26,236 --> 00:26:32,556
Speaker 1: cl which would eventually become Cambridge Analytica. Yes, that Cambridge Analytica.

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00:26:32,676 --> 00:26:35,916
Speaker 1: It has nothing to do with Cambridge University. It was

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Speaker 1: just a little known political consulting firm trying to horn

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Speaker 1: in on the lucrative business of advising presidential campaigns. Yeah,

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Speaker 1: so we're really looking at page legs. And the reason

426
00:26:46,116 --> 00:26:48,476
Speaker 1: we focused it on page likes was there's a few

427
00:26:48,516 --> 00:26:51,116
Speaker 1: papers published at that point that showed that, hey, you

428
00:26:51,156 --> 00:26:54,716
Speaker 1: could take people's page legs and use them to predict

429
00:26:54,756 --> 00:26:58,876
Speaker 1: their personalities with some level of accuracy. The company asked

430
00:26:58,916 --> 00:27:02,716
Speaker 1: Alex if he could classify people by five personality traits

431
00:27:03,076 --> 00:27:08,476
Speaker 1: extra version, agreeableness, openness, and so on use their Facebook

432
00:27:08,556 --> 00:27:11,796
Speaker 1: data to herman which little personality buckets they fell into

433
00:27:12,556 --> 00:27:16,196
Speaker 1: kind of routine stuff for him. Would caught Alex's interest

434
00:27:16,476 --> 00:27:18,916
Speaker 1: was the chance to make other studies of the same people.

435
00:27:19,396 --> 00:27:21,116
Speaker 1: Why do you need that much money to collect the

436
00:27:21,196 --> 00:27:24,996
Speaker 1: data paying participants. So the way we usually recruit participants,

437
00:27:24,996 --> 00:27:27,956
Speaker 1: as would say like, hey, please answer twenty minutes of

438
00:27:28,036 --> 00:27:30,076
Speaker 1: questionnaires for us, and we'll give you a few dollars

439
00:27:30,116 --> 00:27:33,516
Speaker 1: for your time. And in this case we got something

440
00:27:33,516 --> 00:27:36,756
Speaker 1: like two hundred thousand people to go and give us

441
00:27:36,796 --> 00:27:38,716
Speaker 1: twenty minutes of their time, and we paid them around

442
00:27:38,756 --> 00:27:43,396
Speaker 1: four bucks each. He didn't even need to go find

443
00:27:43,436 --> 00:27:47,356
Speaker 1: these people. They found him through websites where people offered

444
00:27:47,356 --> 00:27:50,716
Speaker 1: to be lab rats for researchers in exchange for cash

445
00:27:50,836 --> 00:27:55,196
Speaker 1: or prizes. Alex gave them cash. They gave Alex access

446
00:27:55,236 --> 00:27:57,556
Speaker 1: to their Facebook data, which I guess tells you that

447
00:27:57,596 --> 00:27:59,116
Speaker 1: a lot of people are happy to put a price

448
00:27:59,116 --> 00:28:03,676
Speaker 1: on their privacy. Anyway, Cambridge Analytica's idea wasn't even all

449
00:28:03,716 --> 00:28:07,036
Speaker 1: that original. The Obama campaign claimed to have done the

450
00:28:07,076 --> 00:28:10,636
Speaker 1: same thing with Facebook data back into twelve, though on

451
00:28:10,636 --> 00:28:14,836
Speaker 1: a smaller scale. But Alex figured out pretty quickly just

452
00:28:14,916 --> 00:28:17,316
Speaker 1: how hard it was to do what his client wanted.

453
00:28:18,236 --> 00:28:22,516
Speaker 1: You couldn't really predict much about people using their Facebook data,

454
00:28:22,676 --> 00:28:26,196
Speaker 1: or at least he couldn't. We started asking the question

455
00:28:26,236 --> 00:28:28,956
Speaker 1: of like, well, how often are we right? And so

456
00:28:28,996 --> 00:28:32,596
Speaker 1: there's five personality dimensions, and we said, like, okay, for

457
00:28:32,756 --> 00:28:36,356
Speaker 1: one percentage of people, do we get all five personality

458
00:28:36,836 --> 00:28:40,156
Speaker 1: categories correct? We found it was like one percent. How

459
00:28:40,156 --> 00:28:41,916
Speaker 1: did you even check that? Though? How do you find

460
00:28:41,956 --> 00:28:46,316
Speaker 1: out whether someone is an extrovert? The two hundred thousands

461
00:28:46,396 --> 00:28:50,276
Speaker 1: that provided us to the personality scores, because those terms

462
00:28:50,276 --> 00:28:52,316
Speaker 1: of thousand people to authorize that app filled out the

463
00:28:52,316 --> 00:28:56,236
Speaker 1: personality quiz, and that would be like, okay, let's go

464
00:28:56,316 --> 00:28:59,076
Speaker 1: and see how these people actually answered, and let's see

465
00:28:59,076 --> 00:29:01,396
Speaker 1: what we predicted and we could compare it. So, assuming

466
00:29:01,436 --> 00:29:04,396
Speaker 1: they know their personality and that was right, you got

467
00:29:04,396 --> 00:29:06,516
Speaker 1: it right one percent of the time. One percent of time.

468
00:29:08,156 --> 00:29:11,316
Speaker 1: I'm going to break that down for you. Cambridge Analytica

469
00:29:11,396 --> 00:29:16,356
Speaker 1: had Alex Cogan collecting and compiling Facebook data in a

470
00:29:16,396 --> 00:29:20,076
Speaker 1: way that was incredibly useless. I think we got halfway

471
00:29:20,116 --> 00:29:23,236
Speaker 1: through the project and realize, you know, this probably doesn't

472
00:29:23,276 --> 00:29:25,916
Speaker 1: work that well. But at that point, you know, we're

473
00:29:25,956 --> 00:29:28,876
Speaker 1: contractorally obligated to give them the data and they were

474
00:29:28,916 --> 00:29:32,636
Speaker 1: still interested. But here was the crazy thing. The consulting

475
00:29:32,676 --> 00:29:35,996
Speaker 1: firm didn't care whether it worked or it didn't. They're

476
00:29:35,996 --> 00:29:39,516
Speaker 1: getting paid pots of money by Ted Cruz's presidential campaign,

477
00:29:39,996 --> 00:29:42,876
Speaker 1: who were trying to reach voters on social media. The

478
00:29:42,956 --> 00:29:45,516
Speaker 1: Cruise campaign didn't seem to know that this stuff didn't work.

479
00:29:46,716 --> 00:29:52,676
Speaker 1: With a heavy heart, but with boundless optimism. Then Ted

480
00:29:52,796 --> 00:29:58,276
Speaker 1: Cruz lost the Republican primary to Donald Trump, we are

481
00:29:58,356 --> 00:30:02,916
Speaker 1: suspending our campaign. Cambridge Analytica had used Alex's useless predictions

482
00:30:03,236 --> 00:30:07,436
Speaker 1: to help the loser to lose. Now amazingly, they sold

483
00:30:07,476 --> 00:30:11,076
Speaker 1: their services to the winner. Alex never learned whether the

484
00:30:11,116 --> 00:30:14,356
Speaker 1: Trump campaign actually ever used his data, but in the

485
00:30:14,476 --> 00:30:18,436
Speaker 1: end that didn't matter. And when Donald Trump became president,

486
00:30:19,356 --> 00:30:22,316
Speaker 1: a lot of folks thought incredible had happened. So they

487
00:30:22,356 --> 00:30:26,716
Speaker 1: started looking for incredible explanations. Could the same data have

488
00:30:26,876 --> 00:30:30,116
Speaker 1: been possibly used when this selection? Because like, how else

489
00:30:30,116 --> 00:30:32,796
Speaker 1: could this possibly have happened? So folks are looking for, like,

490
00:30:33,196 --> 00:30:36,876
Speaker 1: where's the evil genius that could have possibly caused all this?

491
00:30:38,596 --> 00:30:42,396
Speaker 1: That was the moment Alex called his old teacher, Daker Keltner,

492
00:30:42,836 --> 00:30:45,436
Speaker 1: who gave him which sounded like good advice. I told

493
00:30:45,476 --> 00:30:48,516
Speaker 1: him like key below profile and just try to stay

494
00:30:48,556 --> 00:30:52,436
Speaker 1: out of the conversation. And that advice mostly worked right

495
00:30:52,516 --> 00:30:55,956
Speaker 1: up until early twenty and eighteen. First, our chief business

496
00:30:55,996 --> 00:31:00,316
Speaker 1: correspondent Rebecca Jarvis has the latest. He's the scientist at

497
00:31:00,316 --> 00:31:03,236
Speaker 1: the heart of the Facebook privacy scandal, and then the

498
00:31:03,316 --> 00:31:06,916
Speaker 1: drama unfolded a researcher at the University of Cambridge. They

499
00:31:06,956 --> 00:31:10,436
Speaker 1: finally realized that I was worn in the Soviet Union

500
00:31:11,076 --> 00:31:20,036
Speaker 1: to collect the data of millions of ali About a

501
00:31:20,036 --> 00:31:22,836
Speaker 1: week before the stories break, the New York Times and

502
00:31:22,916 --> 00:31:26,156
Speaker 1: The Guardian email me with a bunch of questions about

503
00:31:26,276 --> 00:31:29,036
Speaker 1: like the project and also whether I might be a

504
00:31:29,076 --> 00:31:32,836
Speaker 1: Russian spy. Now, I didn't want to ask them, like, guys,

505
00:31:32,916 --> 00:31:35,036
Speaker 1: if I am actually a Russian spy, do you think?

506
00:31:35,076 --> 00:31:37,276
Speaker 1: Like a direct question was going to trip me up,

507
00:31:37,276 --> 00:31:39,076
Speaker 1: And I'm gonna say, you got me, Yes, I'm a

508
00:31:39,116 --> 00:31:45,076
Speaker 1: Russian spy. It's now April twenty and eighteen. Alex Cogan's thinking,

509
00:31:45,516 --> 00:31:48,556
Speaker 1: surely someone will step in and sort this out, some

510
00:31:48,716 --> 00:31:52,796
Speaker 1: neutral third party, some grown up inside the New York Times.

511
00:31:52,836 --> 00:31:56,476
Speaker 1: Maybe someone would just stop and think about it. He

512
00:31:56,636 --> 00:32:00,876
Speaker 1: was an academic using some political consulting money to make

513
00:32:00,996 --> 00:32:05,236
Speaker 1: useless predictions about people's personalities, while also funding his own

514
00:32:05,276 --> 00:32:09,316
Speaker 1: studies on the side. He signed this agreement with Facebook,

515
00:32:09,556 --> 00:32:11,996
Speaker 1: the one that's spelled out how he could interact with

516
00:32:12,036 --> 00:32:15,156
Speaker 1: its users, and the company was okay with everything he'd

517
00:32:15,196 --> 00:32:18,916
Speaker 1: been doing. Facebook had explicitly agreed to let him use

518
00:32:18,996 --> 00:32:22,196
Speaker 1: Facebook data not just for academic research, but for commerce

519
00:32:22,596 --> 00:32:26,236
Speaker 1: if he could find some business use for it. When

520
00:32:26,276 --> 00:32:30,116
Speaker 1: reporters called him, he'd say, look at the agreement. Call Facebook,

521
00:32:30,196 --> 00:32:33,796
Speaker 1: they'll tell you the truth. But it's clear now that

522
00:32:33,876 --> 00:32:36,396
Speaker 1: we didn't do enough to prevent these tools from being

523
00:32:36,476 --> 00:32:39,716
Speaker 1: used for harm as well, and that goes for fake news,

524
00:32:40,196 --> 00:32:43,516
Speaker 1: for foreign interference and elections, and hate speech, as well

525
00:32:43,556 --> 00:32:47,396
Speaker 1: as developers and data privacy. That's Mark Zuckerberg on TV,

526
00:32:48,596 --> 00:32:50,596
Speaker 1: not looking like he wants to tell anybody the truth.

527
00:32:50,956 --> 00:32:55,276
Speaker 1: Facebook goes on the defensive. They do a press release

528
00:32:55,356 --> 00:32:58,836
Speaker 1: basically say like we've banned Kim John Letaca, they we've

529
00:32:58,876 --> 00:33:02,836
Speaker 1: banned Cogan. They basically also say that you know, Cogan

530
00:33:02,956 --> 00:33:05,436
Speaker 1: here told us it was for academocra research and that's

531
00:33:05,436 --> 00:33:07,356
Speaker 1: why we let him do it, which wasn't true at all.

532
00:33:07,596 --> 00:33:09,356
Speaker 1: We need to make sure that people aren't you using

533
00:33:09,356 --> 00:33:11,756
Speaker 1: it to harm other people. Facebook wanted people to believe

534
00:33:11,756 --> 00:33:14,756
Speaker 1: it was a victim of this data thief, when in fact,

535
00:33:14,836 --> 00:33:17,316
Speaker 1: it had given Alex's permission to do exactly what he did.

536
00:33:18,396 --> 00:33:21,596
Speaker 1: But then Facebook was created to be an unrefereed space.

537
00:33:22,356 --> 00:33:24,716
Speaker 1: It allowed its users to do and say pretty much

538
00:33:24,716 --> 00:33:29,796
Speaker 1: whatever they pleased and took no responsibility for the consequences. Now,

539
00:33:29,836 --> 00:33:33,236
Speaker 1: the world was furious with Facebook for not refing itself,

540
00:33:34,156 --> 00:33:37,316
Speaker 1: and so it panicked and look for someone else to blame.

541
00:33:38,276 --> 00:33:40,436
Speaker 1: Alex Cogan had set out in life to study our

542
00:33:40,476 --> 00:33:43,596
Speaker 1: positive emotions. He now got his lesson in the other kind,

543
00:33:44,516 --> 00:33:48,756
Speaker 1: anger mistrust. All these reporters were now calling him to

544
00:33:48,796 --> 00:33:52,796
Speaker 1: ask these very weird, hostile questions, like why it changed

545
00:33:52,836 --> 00:33:57,076
Speaker 1: his last name after he'd gotten married. We wanted to

546
00:33:57,116 --> 00:34:01,276
Speaker 1: find something that symbolize both our religious sides or a

547
00:34:01,356 --> 00:34:04,836
Speaker 1: scientific sites, because we're both scientists and religious, and we

548
00:34:04,996 --> 00:34:08,596
Speaker 1: landed this idea of light and they're like, oh, spectrum

549
00:34:09,276 --> 00:34:11,156
Speaker 1: like and then we heard the last name Specter, and

550
00:34:11,156 --> 00:34:13,036
Speaker 1: I'm like, oh, that's really cool, let's do that. So

551
00:34:13,076 --> 00:34:17,436
Speaker 1: we change your last name to Specter. Bad luck hab it.

552
00:34:18,076 --> 00:34:22,036
Speaker 1: Specter is also the evil organization from James Bond. I

553
00:34:22,156 --> 00:34:25,356
Speaker 1: got a lot of questions from a lot of journalists

554
00:34:25,356 --> 00:34:28,716
Speaker 1: saying like, Hey, this whole Specter thing is mighty suspicious.

555
00:34:31,116 --> 00:34:33,796
Speaker 1: I just say this that if you're planning to do

556
00:34:33,836 --> 00:34:39,076
Speaker 1: something sinister, if you're even vaguely considering the possibility, the

557
00:34:39,156 --> 00:34:41,916
Speaker 1: last thing you should do is change your last name

558
00:34:41,956 --> 00:34:46,996
Speaker 1: to Specter. It's like naming a restaurant sam and Ella.

559
00:34:47,396 --> 00:34:50,956
Speaker 1: Maybe that's just me. All the little details of Alex

560
00:34:51,036 --> 00:34:56,396
Speaker 1: Cogan's life had now become evidence for the prosecution. No

561
00:34:56,396 --> 00:34:58,476
Speaker 1: one even had to come out and say that Alex

562
00:34:58,556 --> 00:35:02,556
Speaker 1: Cogan was a spy. The Guardian ran graphics and little

563
00:35:02,676 --> 00:35:06,196
Speaker 1: arrows pointing from a picture of red Square to a

564
00:35:06,276 --> 00:35:11,556
Speaker 1: picture of Alex Cogan. What the Russia connection? I woke

565
00:35:11,636 --> 00:35:14,916
Speaker 1: up that day too, like two hundred emails from pretty

566
00:35:14,996 --> 00:35:18,756
Speaker 1: much every outlet in the world. CNNs starts trying to

567
00:35:18,796 --> 00:35:21,996
Speaker 1: track me down, Like I started giving phone calls from

568
00:35:21,996 --> 00:35:24,116
Speaker 1: like my old house in San Francisco that CNN is

569
00:35:24,156 --> 00:35:26,756
Speaker 1: like poking around trying to find me, and then they

570
00:35:26,756 --> 00:35:30,476
Speaker 1: show up at my door. The story of Alex Cogan

571
00:35:30,756 --> 00:35:35,116
Speaker 1: and Cambridge Analytica went viral before it ever really got

572
00:35:35,196 --> 00:35:38,836
Speaker 1: checked for whether it made any sense. It was refed

573
00:35:38,836 --> 00:35:42,356
Speaker 1: by the crowd. The crowd just decided that it liked

574
00:35:42,396 --> 00:35:46,876
Speaker 1: the story and ran with it. The US government started

575
00:35:46,996 --> 00:35:50,236
Speaker 1: knocking my door. We got, you know, questions from the

576
00:35:50,356 --> 00:35:56,116
Speaker 1: US Senate, the House, and etc. Etc. The British Parliament

577
00:35:56,196 --> 00:35:58,596
Speaker 1: reached out and I learned you can't really talk to

578
00:35:58,636 --> 00:36:01,556
Speaker 1: the government as a private citizen. So like financially like

579
00:36:01,676 --> 00:36:04,956
Speaker 1: completely wiped me out and like massive debt. Now in

580
00:36:05,036 --> 00:36:08,716
Speaker 1: terms of the legal bills, as far as the academic career,

581
00:36:09,156 --> 00:36:13,796
Speaker 1: pretty much over. A promising academic career went poof, just

582
00:36:14,156 --> 00:36:17,356
Speaker 1: like that. All he's got left is the possibility of

583
00:36:17,396 --> 00:36:21,676
Speaker 1: writing a memoir of the experience and a lawsuit against Facebook,

584
00:36:21,836 --> 00:36:24,996
Speaker 1: accusing the company of defamation, which he filed a few

585
00:36:24,996 --> 00:36:30,076
Speaker 1: months after we spoke. I met with a guy who

586
00:36:30,956 --> 00:36:33,156
Speaker 1: is doing a documentary about all of this, and he's like,

587
00:36:33,796 --> 00:36:36,236
Speaker 1: you know, it's crazy. I was warned, and I'm not

588
00:36:36,236 --> 00:36:38,316
Speaker 1: gonna tell you by who, but it's somebody prominence. But

589
00:36:38,476 --> 00:36:40,916
Speaker 1: I was warned when I'm talking to you to be

590
00:36:40,956 --> 00:36:44,436
Speaker 1: really careful because you're a trained covert agent from Russia

591
00:36:44,516 --> 00:36:50,636
Speaker 1: and you would out my phone. I think of Alex

592
00:36:50,716 --> 00:36:53,676
Speaker 1: Cogan as a curious kind of victim, even if he

593
00:36:53,756 --> 00:36:57,716
Speaker 1: refuses to sound anything but cheery about his situation. He's

594
00:36:57,716 --> 00:37:00,036
Speaker 1: what happens when the refs are banished from the news,

595
00:37:00,836 --> 00:37:03,276
Speaker 1: when people are encouraged to believe whatever it is they

596
00:37:03,316 --> 00:37:06,516
Speaker 1: want to believe. It's not that the news was once

597
00:37:06,556 --> 00:37:10,116
Speaker 1: perfectly refereed and now it's not, or that there weren't

598
00:37:10,116 --> 00:37:13,076
Speaker 1: ever fake stories, or that people haven't always believed all

599
00:37:13,196 --> 00:37:18,236
Speaker 1: kinds of bullshit. But there's an obvious antidote, the neutral

600
00:37:18,316 --> 00:37:23,596
Speaker 1: third party, the independent authority, the referee who makes it

601
00:37:23,636 --> 00:37:27,236
Speaker 1: more difficult, if only just a little bit, for an

602
00:37:27,276 --> 00:37:32,116
Speaker 1: easy lie to replace a complicated truth. Yet the job

603
00:37:32,196 --> 00:37:36,396
Speaker 1: doesn't exist. The market doesn't want some neutral third party

604
00:37:36,596 --> 00:37:39,876
Speaker 1: interfering with our ability to create our own truths, to

605
00:37:40,036 --> 00:37:45,196
Speaker 1: render our own meanings, to construct our own realities as

606
00:37:45,196 --> 00:38:01,596
Speaker 1: we decline, stage by stage against the Rules. Is brought

607
00:38:01,596 --> 00:38:05,156
Speaker 1: to you by Pushkin Industries. The show's produced by Audrey

608
00:38:05,156 --> 00:38:09,396
Speaker 1: Dilling and Catherine Girardote, with research assistance from Zoe Oliver

609
00:38:09,516 --> 00:38:15,116
Speaker 1: Gray and Beth Johnson. Our editor is Julia Barton. Mia

610
00:38:15,196 --> 00:38:19,276
Speaker 1: Lobell is our executive producer. Our theme was composed by

611
00:38:19,356 --> 00:38:23,556
Speaker 1: Nick Burttell, with additional scoring by Seth Samuel, mastering by

612
00:38:23,636 --> 00:38:28,236
Speaker 1: Jason Gambrel. Our show was recorded by Tofa Ruth at

613
00:38:28,316 --> 00:38:33,036
Speaker 1: Northgate Studios at UC Berkeley. Special thanks to our founders,

614
00:38:33,196 --> 00:38:56,636
Speaker 1: Jacob Weisberg and Malcolm Gladwell. Do you mean an example

615
00:38:56,676 --> 00:39:00,716
Speaker 1: of the state something that's at stage one now? Using

616
00:39:02,116 --> 00:39:08,236
Speaker 1: climatic in the sense climactic, this was the climatic point

617
00:39:08,276 --> 00:39:14,556
Speaker 1: of the play. Well climate yeah, they're both words. I

618
00:39:15,396 --> 00:39:20,276
Speaker 1: absolutely u and if you if you take the phrase

619
00:39:20,436 --> 00:39:24,276
Speaker 1: so anti climactic is the word is an anti climax.

620
00:39:25,196 --> 00:39:31,956
Speaker 1: But if you search anti climatic versus anti climactic, the

621
00:39:32,116 --> 00:39:35,556
Speaker 1: ratio and that's the you have to contextualize these searches.

622
00:39:35,676 --> 00:39:39,556
Speaker 1: There's no reason to use anti climatic at all. But

623
00:39:39,596 --> 00:39:44,036
Speaker 1: it's twenty eight to one in print sources anti climactic

624
00:39:44,356 --> 00:39:47,316
Speaker 1: in favor of anti climactic. But the fact that the

625
00:39:47,356 --> 00:39:51,716
Speaker 1: other one appears once every twenty eight times, that yeah,

626
00:39:51,756 --> 00:39:56,556
Speaker 1: it is. So this is like linguistic epidemiology. It begins

627
00:39:56,596 --> 00:40:00,716
Speaker 1: to spread. A lot of us have snakes in the grass.

628
00:40:01,316 --> 00:40:05,716
Speaker 1: We call them garter snakes, and garter snakes have little

629
00:40:06,116 --> 00:40:08,796
Speaker 1: stripes on them that look like garters. But a lot

630
00:40:08,796 --> 00:40:11,356
Speaker 1: of people people misheard that and started saying garden snake.

631
00:40:11,556 --> 00:40:13,956
Speaker 1: They thought it was it's a garden it's a register, regular,

632
00:40:14,116 --> 00:40:19,556
Speaker 1: harmless garden snake. Well it's a garter snake. That is

633
00:40:21,076 --> 00:40:24,196
Speaker 1: uh wow, Well that's a problem. That's eight to one

634
00:40:24,236 --> 00:40:26,516
Speaker 1: because if that snake in the garden is a rattlesnake,

635
00:40:26,916 --> 00:40:30,036
Speaker 1: that's right, there could be a real I've got I've

636
00:40:30,076 --> 00:40:32,076
Speaker 1: got a garden snake out there. Oh good, I don't

637
00:40:32,116 --> 00:40:35,396
Speaker 1: have to wear any protective clothing. I'll go catch it. Well,

638
00:40:35,476 --> 00:40:38,956
Speaker 1: you know that these are problems people would say you

639
00:40:38,956 --> 00:40:40,996
Speaker 1: and I just made that up. Give me an example

640
00:40:40,996 --> 00:40:45,716
Speaker 1: of the stage stage four um misspelling minuscule as if

641
00:40:45,756 --> 00:40:49,836
Speaker 1: it were miniskirt minuscules m I n us culi. But

642
00:40:49,916 --> 00:40:54,236
Speaker 1: that's two to one in print. Now or anti vinin.

643
00:40:54,516 --> 00:40:58,196
Speaker 1: Now here's one anti vinen. If you get bitten by

644
00:40:58,356 --> 00:41:01,516
Speaker 1: not a garter snake, but by a rattlesnake, you need

645
00:41:01,596 --> 00:41:06,316
Speaker 1: anti vinin v E N I N. But the noun

646
00:41:06,636 --> 00:41:10,196
Speaker 1: for what the snake puts into you is them, And

647
00:41:10,236 --> 00:41:14,476
Speaker 1: so a lot of people you know this is is

648
00:41:14,476 --> 00:41:18,196
Speaker 1: it really worth preserving? I don't know. It's traditional English

649
00:41:18,276 --> 00:41:23,636
Speaker 1: anti venin, and it comes from a Latin form. But

650
00:41:23,836 --> 00:41:28,156
Speaker 1: people have started saying anti venom, and that one is

651
00:41:28,516 --> 00:41:31,396
Speaker 1: one point two to one in favor of anti venom.

652
00:41:31,756 --> 00:41:35,236
Speaker 1: But that's one where I continue to recommend the traditional

653
00:41:35,276 --> 00:41:38,236
Speaker 1: form anti vinin. So you go into the garden and

654
00:41:38,316 --> 00:41:40,036
Speaker 1: you pick up the snake because you think it's a

655
00:41:40,076 --> 00:41:42,116
Speaker 1: garden snake, and you're a bit by the rattlesnake, and

656
00:41:42,156 --> 00:41:44,756
Speaker 1: you go you're bitten. You're bitten by the bit, thank

657
00:41:44,796 --> 00:41:47,276
Speaker 1: you very much, bitten by the rattlesnake, and you're taking

658
00:41:47,316 --> 00:41:49,036
Speaker 1: to the hospital and by the time they figure out

659
00:41:49,036 --> 00:41:50,916
Speaker 1: what you're trying to ask for, because you're asking for

660
00:41:50,956 --> 00:41:54,236
Speaker 1: anti venom and they don't have any, you're dead. Yeah,

661
00:41:54,236 --> 00:41:56,516
Speaker 1: because you mispronounced. Sorry, we're not giving you any. All

662
00:41:56,556 --> 00:41:59,316
Speaker 1: we have is anti vinin. We don't have any anti venom.

663
00:42:00,156 --> 00:42:02,476
Speaker 1: And by the way, I don't normally correct people, but

664
00:42:02,596 --> 00:42:05,876
Speaker 1: forgive me for that that bitten thing, Thank you very much. Sure,