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Speaker 1: Pushkin.

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Speaker 2: In the middle of our preparations for season seven of

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Speaker 2: This Your Favorite podcast, the Revisionist History team got on

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Speaker 2: a train to Philadelphia, four of us carrying props, recording equipment,

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Speaker 2: and extra microphones. Our destination the Gothic, ivy covered Cathedral

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Speaker 2: of Higher Learning that is the University of Pennsylvania. And

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Speaker 2: why did we go Because we had cooked up a

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Speaker 2: little experiment and we were curious to see how it

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Speaker 2: would fly.

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Speaker 3: Thank you welcome.

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Speaker 2: We commandeered one of the main lecture halls at the

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Speaker 2: Wharton School. I did seventy five or so students, all seniors, smart,

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Speaker 2: focused discipline, future masters of the universe, and asked them

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Speaker 2: to answer ten simple questions, such as, how many years

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Speaker 2: of your K twelve education were a public school and

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Speaker 2: how many were a private school at the time of

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Speaker 2: your graduation from high school? How many continents had you

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Speaker 2: visited at any point during your middle school and high

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Speaker 2: school years? Did your parents provide you with a private tutor?

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Speaker 4: Are you doing that?

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Speaker 2: Mister pretty good? Joined myself. I looked out at Rose

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Speaker 2: and rose at eager students hunched over their deaths in anticipation,

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Speaker 2: took a deep breath and began So my name is

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Speaker 2: Malcolm Gladwell. I am the host of the podcast Revision's History.

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Speaker 2: The theme of this season of Revision's History is experiments,

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Speaker 2: and one of the experiments of this season involves all

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Speaker 2: of you. So, you guys are guinea pigs. Yes, guinea pigs,

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Speaker 2: because in the manner of all guinea pigs, they were

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Speaker 2: entirely in the dark about what we had in store

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Speaker 2: for them. And as you probably guessed from some of

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Speaker 2: the questions that you were given, what I'm trying to

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Speaker 2: do is I'm conducting an experimental investigation into the nature

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Speaker 2: of the privilege of the people in this room. The

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Speaker 2: students quickly finished the questionnaires and put their names and

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Speaker 2: birth dates at the top. My producers, Elowise and Harrison

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Speaker 2: are sitting at a big table at the front of

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Speaker 2: the room, in full view of all the guinea pigs.

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Speaker 2: They go through the completed questionnaires one by one and

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Speaker 2: use the answers to generate a number, a score, which

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Speaker 2: they write on a giant white sticker with a big

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Speaker 2: fat sharpie. And now the real experiment begins. I'm going

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Speaker 2: to sign every one of you a number. If they

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Speaker 2: can figure out what their number means. They will understand

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Speaker 2: something essential about how broken the system was that propelled

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Speaker 2: them to the IVY League and how to fix it.

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Speaker 2: Just peel off the back and I'd like you to

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Speaker 2: fix the sticker to your chest so we can all

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Speaker 2: see each other's numbers. You're going to look around the room,

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Speaker 2: see everyone else's numbers, see your number, and hopefully that

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Speaker 2: will aid you in your investigation of what exactly the

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Speaker 2: nature of this experiment is. I'll just read the students

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Speaker 2: sat there with their numbers stuck to their chests, looking

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Speaker 2: around in befuddlement, trying to make sense of everyone's score.

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Speaker 2: I tried to help them figure it out, gave them

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Speaker 2: hints nudges. First, think about this. I gave you a

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Speaker 2: series of questions. Some of those questions involved a yes

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Speaker 2: or no answer. So you saw two people, Hellowy's and Harrison,

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Speaker 2: who quite quickly, in the space of about five minutes

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Speaker 2: ten minutes, went through seventy five or so responses and

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Speaker 2: were able to very quickly and easily assign you a number.

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Speaker 2: So think about this. Logically, it wasn't a complex algorithm, right,

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Speaker 2: there was no computer used Alowys, How long would you

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Speaker 2: say you were spending in, Harrison, how long would you

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Speaker 2: spending on each questionnaire? Six seconds?

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Speaker 4: Five?

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Speaker 2: Six seconds? Okay, that's a clue, guys, let's go come on. Hi.

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Speaker 2: My name is Abe.

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Speaker 4: They might have just looked at the zip code, because

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Speaker 4: that's a pretty good predictor of privilege just in and

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Speaker 4: of itself.

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Speaker 2: Abe has derived his hypothesis from question six, what is

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Speaker 2: the zip code your family lived in during your high

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Speaker 2: school years? Perhaps he speculates the number on his chest

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Speaker 2: with some kind of complex, mysterious derivative of his zip code.

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Speaker 5: I didn't see if you had a computer, but if

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Speaker 5: you did.

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Speaker 2: There was no El's Was there a computer?

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Speaker 6: Now?

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Speaker 7: I did have to use a calculator.

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Speaker 2: Gota Abraham, with all due respect, are you suggesting that

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Speaker 2: Eloise and Harrison had memorized every zip code? It's plausible

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Speaker 2: they're very smart. Not that smart, I'm Zach. I think

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Speaker 2: it really has to do strictly with the private versus

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Speaker 2: public education system in the US. Nope, that's not what

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Speaker 2: we were looking for. Hi, my name is Joseph.

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Speaker 8: A question that I thought was very interesting on there

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Speaker 8: was about if you have any siblings and if so,

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Speaker 8: how many.

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Speaker 2: Nope, not that either.

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Speaker 6: Hi, I'm Kaylee.

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Speaker 9: One that I don't think I've ever been asked in

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Speaker 9: relation to this was if I drank when I was

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Speaker 9: in high school?

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Speaker 1: What age did I get drunk at?

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Speaker 2: Kayley's referring to question number nine in high school, did

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Speaker 2: you drink alcohol? And if yes, when did you first

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Speaker 2: get drunk? Could you come up with any reason why

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Speaker 2: I would have asked that question? Or do you think

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Speaker 2: that's just one of the ones that I'm blowing smoke on?

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Speaker 9: I have my own hypothesis, but I can't.

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Speaker 2: Oh, come on, what if I'm wrong? This is all

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Speaker 2: about being wrong? Oh, this is all about being wrong.

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Speaker 2: Once upon a time, in two thousand and eight, I

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Speaker 2: wrote a book called Outliers, the first chapter of which

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Speaker 2: was devoted to a phenomenon discovered in the nineteen eighties

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Speaker 2: by the Canadian psychologist Roger Barnsley. Here's some of what

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Speaker 2: I wrote. The explanation for who gets to the top

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Speaker 2: of the hockey world is a lot more interesting and

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Speaker 2: complicated than it looks. Good, Lord, I do not sound

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Speaker 2: like I'm enjoying reading my own book listening to this

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Speaker 2: part of the Outliers audiobook. Now, I'll admit I have

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Speaker 2: some regrets about that chapter. We'll get to that, I promise. Anyway,

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Speaker 2: it occurred to me as I planned our trip to

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Speaker 2: Philly that I should talk to Barnsley again and go

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Speaker 2: over his discovery one more time, make sure I understood everything.

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Speaker 2: So I called him up and asked him to retell

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Speaker 2: the story of how in the early nineteen eighties he

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Speaker 2: and his wife Paula stumbled upon what has come to

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Speaker 2: be known as their relative age effect.

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Speaker 7: We were living in Lethbvirge, Alberta, and we went to

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Speaker 7: a Junior A hockey team. It was the Lethbridge Broncos.

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Speaker 7: At that time.

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Speaker 2: Barnsley's wife Paula started reading the game program, which had

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Speaker 2: the rosters of both teams listed in it.

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Speaker 7: Paula said over to me, Roger, when do you think

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Speaker 7: all of these hockey players were born? And I remember

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Speaker 7: thinking to myself, well, you know, that's kind of a

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Speaker 7: silly question. So I did a quick calculation. I said,

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Speaker 7: you know, Paula, they're average age eighteen. It's about nineteen

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Speaker 7: eighty two, so they're probably all born in around nineteen

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Speaker 7: sixty four. And she said, no, no, no, I'm not

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Speaker 7: talking about the year. I'm talking about the month, and

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Speaker 7: I said, what are you talking about? And she opened

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Speaker 7: up the page of the program where they had listed

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Speaker 7: the roster of the team, and it just jumped out

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Speaker 7: at us, just jumped out that the majority of these

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Speaker 7: players were January, February, March, and then you seem to

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Speaker 7: get the aud April in May, and very few in

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Speaker 7: the fall. And I said, my goodness, that's just remarkable.

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Speaker 2: He went home and expanded his search further. Everywhere he

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Speaker 2: looked in competitive hockey, same thing. For some reason, most

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Speaker 2: players were born in the first part of the year.

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Speaker 7: And that's when that famous forty thirty twenty ten by

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Speaker 7: the Courters of the Year showed up.

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Speaker 2: The famous forty thirty twenty ten phenomenon that he's talking

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Speaker 2: about is what in Outliers I referred to as the

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Speaker 2: iron law of Canadian hockey. Quote. In any elite group

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Speaker 2: of hockey players, the very best of the best, forty

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Speaker 2: percent of the players will have been born between January

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Speaker 2: and March, thirty percent between April and June, twenty percent

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Speaker 2: between July and September, and ten percent between October and

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Speaker 2: December end.

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Speaker 8: Quote.

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Speaker 2: Now why is this. It's because Canada is obsessed with hockey,

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Speaker 2: and coaches start picking players for all star traveling squads

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Speaker 2: at the age of nine or ten. Since the eligibility

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Speaker 2: cut off for Canadian hockey is January first, that means

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Speaker 2: the coaches are choosing among nine year olds who are

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Speaker 2: as much as twelve months apart, and twelve months age

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Speaker 2: difference at the age of nine is a lot. The

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Speaker 2: January kids are bigger and stronger and more coordinated than

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Speaker 2: the December kids, which means that the January kid is

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Speaker 2: more likely to be chosen by the coaches for the

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Speaker 2: traveling squad, which means in turn that they will practic

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Speaker 2: two or three times as often, play more games, have

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Speaker 2: better coaches, better competition than the kids left behind, and

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Speaker 2: what began as a completely arbitrary advantage based on a

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Speaker 2: quirk of birthdays turns over time into a real advantage.

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Speaker 2: The same phenomenon holds to in other sports, soccer, swimming,

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Speaker 2: you name it. You can find the relative age effect everywhere,

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Speaker 2: and of course it also applies to the classroom. Teachers

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Speaker 2: aren't any better than coaches at disentangling ability from maturity,

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Speaker 2: so relatively older kids in elementary and middle school end

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Speaker 2: up getting more encouragement they tend to get better grades,

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Speaker 2: and they are more likely to be chosen for things

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Speaker 2: like gifted and Talented programs. Meanwhile, relatively younger kids are

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Speaker 2: more likely to be diagnosed with learning disorders or flagged

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Speaker 2: for problem behavior. I cannot tell you how many parents

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Speaker 2: have come up to me over the years and said,

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Speaker 2: because I read your book Outliers, I held my kid

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Speaker 2: back from starting school, and it was the best decision

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Speaker 2: I ever made.

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Speaker 9: Of course it was.

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Speaker 2: But parents holding their kids back doesn't solve the problem.

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Speaker 2: It just creates a relative age effect arms race. There's

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Speaker 2: a fancy private school near me where so many parents

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Speaker 2: of younger children have held their kids back that now

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Speaker 2: the parents of the formerly eldest children have responded by

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Speaker 2: holding their kids back, whereupon the first out of parents

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Speaker 2: are increasingly holding their kids back a second time, meaning

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Speaker 2: that there is at least a theoretical possibility that in

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Speaker 2: the most competitive corners of American private education, some kids

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Speaker 2: may never graduate from high school. Maybe I should have

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Speaker 2: seen on that coming when I wrote Outliers. I should

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Speaker 2: have made it clear that I was not trying to

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Speaker 2: teach neurotic, upper middle class helicopter parents how to gain

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Speaker 2: the system. I just wanted schools and sports leagues to

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Speaker 2: stop behaving like idiots. So Barnsley's paper on relative age

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Speaker 2: effect came out in nineteen eighty five Outliers, which was

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Speaker 2: I think the first time Barnsley's work got wide publicity,

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Speaker 2: was published in two thousand and eight. The world has

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Speaker 2: been alerted for decades to the fact that all kinds

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Speaker 2: of supposedly meritocratic systems have been hijacked. Has anything changed?

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Speaker 2: You're in front of your computer. Oh, I put the

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Speaker 2: question to Roger Barnsley, the og of relative age effect research.

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Speaker 2: What have we learned? Can you google the roster of

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Speaker 2: the Canadian junior hockey team national hockey team for twenty

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Speaker 2: twenty one twenty two, the current roster.

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Speaker 7: I'll do it right now, and.

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Speaker 2: I want you to go down the list of the forwards.

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Speaker 2: Just use the forwards for the sake of simplicity, And

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Speaker 2: I want you to to just read the twenty one

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Speaker 2: months of birth of the forwards on the current Canadian

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Speaker 2: junior national hockey team.

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Speaker 7: Their birthdates are just their names.

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Speaker 2: I just want their birth months, okay, to see And

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Speaker 2: then Barnsley repeated what his wife Paula did decades ago

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Speaker 2: at the Lethbridge Broncos hockey game. He listed the birth

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Speaker 2: months by number of the members of the national junior

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Speaker 2: hockey team. Listen for birth months of seven or higher.

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Speaker 7: Two ten, one, one, one, two eleven, eight, four two ten, five, four, five,

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Speaker 7: two six one three one one five three two four

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Speaker 7: seven to one.

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Speaker 2: It's the same thing, the same thing. They've learned nothing.

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Speaker 2: It's the same phenomenon. You saw, you saw it this

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Speaker 2: forty years ago. The iron law of Canadian hockey is

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Speaker 2: still an iron law.

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Speaker 7: Isn't that funny? Funny at all? It's depressing, very depressing,

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Speaker 7: very depressing.

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Speaker 2: Here we are both Canadians, we are we are citizens

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Speaker 2: of a country that cares more about hockey excellence than

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Speaker 2: anything else, must be clear, anything else. And we are

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Speaker 2: leaving an astonishing amount of talent on the table, exactly

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Speaker 2: and refusing to learn. What of Canada's own prominent academics

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Speaker 2: forty years ago said to the hockey establishment, what are

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Speaker 2: you doing? Yeah, that's right, and they didn't. They haven't

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Speaker 2: done anything. Canadian hockey hasn't done anything. But maybe revisionist

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Speaker 2: history can the inspiration for the revisionist history Warton School

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Speaker 2: relative age effect experiment came to me when I was

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Speaker 2: talking to Adam Kelly, former footballer turned university professor. Kelly

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Speaker 2: is a disciple of Roger Barnsley. He works with sports

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Speaker 2: leagues to help them solve their age related problems, like

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Speaker 2: England's basketball Federation, which spent a small fortune setting up

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Speaker 2: regional centers to identify promising players.

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Speaker 3: We looked at the proportion of players who were selected

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Speaker 3: into those talent centers across at the nation, and that.

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Speaker 7: Was at age.

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Speaker 3: Groups selected from thirteen to fifteen, both at male and female,

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Speaker 3: and those who were born in birth quarter one were

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Speaker 3: ten times more likely to be selected ten times.

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Speaker 2: Birth quarter one is the three months closest to the

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Speaker 2: English basketball eligibility cutoff date.

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Speaker 3: Which is absolutely crazy, isn't it?

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Speaker 2: Same old story. The talent spotters thought they were picking

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Speaker 2: the most promising players, but in fact they were just

256
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Speaker 2: picking the oldest kids because the oldest kids were, of

257
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Speaker 2: course the tallest and most coordinated. Anyway, Kelly's also thought

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Speaker 2: a lot about education.

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Speaker 3: Why is everyone taking their exam at exactly the same time.

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Speaker 3: Surely we should all be taking it at that same

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Speaker 3: time within our lifespan. So if you're born in August,

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Speaker 3: you're taking your exam almost twelve months earlier than someone

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Speaker 3: who's born in September. So that person's had twelve months

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Speaker 3: more learning.

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Speaker 2: Than you, which is super obvious when you think about it.

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Speaker 2: In New York State, all the big elementary school math

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Speaker 2: and English standardized tests are in late March early April.

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Speaker 2: We're talking third graders, eight and nine year olds. At

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Speaker 2: that age, kids get smarter every week. Yet we're trying

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Speaker 2: to assess kids by their test scores, and some of

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Speaker 2: the kids were judging have been around as much as

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Speaker 2: a year longer than other kids. Why don't we have

273
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Speaker 2: the January kids all take the test in January, and

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Speaker 2: the February kids in February, and on and on down

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Speaker 2: the line. I have no idea, honestly no idea. So

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Speaker 2: I gathered the research arm of Revisionist History with our props,

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Speaker 2: recording equipment, and extra microphones and headed for the Gothic,

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Speaker 2: ivy covered Cathedral of Higher Learning that is the University

279
00:17:28,156 --> 00:17:31,636
Speaker 2: of Pennsylvania to see if a group of really, really

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Speaker 2: smart young people can figure out the importance of the

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Speaker 2: month when they happen to be born turn all your

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Speaker 2: pieces of favor. That's what your name of the toms,

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Speaker 2: and once you're finished, we will collect them and then

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Speaker 2: we will commence the exercise. So we give out our

285
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Speaker 2: elaborate questionnaire, but secretly, all we're interested in is people's birthdays,

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Speaker 2: and then Elouise and Harrison go through each questionnaire and

287
00:18:02,036 --> 00:18:06,036
Speaker 2: use the birthdays to do a simple calculation. Technically, the

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Speaker 2: youngest you could be as a college senior at the

289
00:18:08,556 --> 00:18:11,676
Speaker 2: time of our EXPERIMID was to be born in September

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Speaker 2: two thousand and one.

291
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Speaker 1: So if you were.

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Speaker 2: Born then you got a zero zero birth privilege. If

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00:18:18,796 --> 00:18:21,996
Speaker 2: you were one month older, born in August two thousand

294
00:18:22,036 --> 00:18:25,396
Speaker 2: and one, you got a one. July two thousand and

295
00:18:25,396 --> 00:18:28,836
Speaker 2: one you got a two, and so on. The higher

296
00:18:28,916 --> 00:18:32,316
Speaker 2: the number on the sticker, the older the student wearing it.

297
00:18:33,196 --> 00:18:35,676
Speaker 2: We even had a contingency for students who might have

298
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Speaker 2: skipped a grade somewhere along the way. You get a

299
00:18:38,276 --> 00:18:41,676
Speaker 2: negative number if you were younger than the expected age

300
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Speaker 2: of a college senior. First thing we found out there

301
00:18:45,556 --> 00:18:48,476
Speaker 2: were no negative numbers in the room. Back when I

302
00:18:48,516 --> 00:18:50,796
Speaker 2: was in college, I knew dozens of people who had

303
00:18:50,796 --> 00:18:55,156
Speaker 2: skipped grades. Apparently that doesn't happen much anymore. But it

304
00:18:55,196 --> 00:19:07,276
Speaker 2: was worse than that. There's no zero. They were a one, two, twos, threes, fours, fives,

305
00:19:08,436 --> 00:19:13,716
Speaker 2: anyone less than ten stand up. One student finally stood

306
00:19:13,796 --> 00:19:16,276
Speaker 2: up in a back row, a college senior who was

307
00:19:16,316 --> 00:19:20,076
Speaker 2: a few months shy of her twenty second birthday. Or

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00:19:20,116 --> 00:19:22,796
Speaker 2: you're ten, Oh, a ten in a back row. Oh,

309
00:19:22,836 --> 00:19:26,276
Speaker 2: another ten emerges. We've got the twelve, We've got these twelves,

310
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Speaker 2: We've got some tens. Take a look. This is bananas.

311
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Speaker 2: This is as bad as the Canadian national junior hockey team.

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Speaker 2: In our sample of students from one of the world's

313
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Speaker 2: most selective universities, there were no young seniors, none, not

314
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Speaker 2: even close. There was no one at all who had

315
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Speaker 2: been born in two thousand and one, which is the

316
00:19:46,116 --> 00:19:48,276
Speaker 2: year you would expect most seniors to be born in.

317
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Speaker 2: At one point, a student started talking about her experience

318
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Speaker 2: in a Gifted and talented program. So I asked for

319
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Speaker 2: a show of hands. Do you mind me asking how

320
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Speaker 2: many of you guys were were in gifted in town

321
00:20:00,076 --> 00:20:05,196
Speaker 2: to programs? Wow? Basically all of you, which makes sense right.

322
00:20:05,956 --> 00:20:08,836
Speaker 2: These were a group of relatively old students and being

323
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Speaker 2: real to the older makes it more likely to get

324
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Speaker 2: into a gifted and talented program, and getting into a

325
00:20:14,516 --> 00:20:17,236
Speaker 2: gifted and talented program makes it more likely to get

326
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Speaker 2: into a school like Penn, which is why a group

327
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Speaker 2: of seniors that day at Wharton were all really old.

328
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Speaker 2: What begins as arbitrary advantage hardens into privilege, a simple

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Speaker 2: fact about their own success that our students still hadn't

330
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Speaker 2: figured out. I'm going to hear another clue, guys, the

331
00:20:43,436 --> 00:20:48,116
Speaker 2: particular dimension of privilege we're interested in measuring, I'm going

332
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Speaker 2: to say, with a great deal of certainty, is in

333
00:20:51,916 --> 00:20:57,436
Speaker 2: this room the most significant form of privilege or lack

334
00:20:57,476 --> 00:21:02,796
Speaker 2: of it, that you would have experienced as students. At

335
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Speaker 2: this point, I've pulled out all the stops trying to

336
00:21:05,996 --> 00:21:09,276
Speaker 2: help them. I've had people with the highest numbers stand up.

337
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Speaker 2: At one point, I made everyone with the number over

338
00:21:11,676 --> 00:21:14,556
Speaker 2: twenty get up from their seats and line up against

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Speaker 2: the wall. They were still guessing, but it was like

340
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Speaker 2: they were throwing darts with a blindfold on.

341
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Speaker 6: There were pretty clear demographic similarity at the top end

342
00:21:25,396 --> 00:21:31,476
Speaker 6: of the spectrum. Racially was the most obvious in my eyes. Yeah,

343
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Speaker 6: but also just in general that there were very few

344
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Speaker 6: at the low end of the spectrum was also noteworthy.

345
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Speaker 2: How do you feel about being in the higher number

346
00:21:41,076 --> 00:21:42,836
Speaker 2: group as opposed to the lower number group.

347
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Speaker 6: I mean, it's just a fact, like it is.

348
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Speaker 1: It's an I.

349
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Speaker 6: Would say, I'm coming into it. I I'm aware of

350
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Speaker 6: my privilege as a white woman, but I think it's

351
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Speaker 6: about what you do with that privilege that's important.

352
00:22:01,876 --> 00:22:05,116
Speaker 2: And then, after forty minutes of floundering in the shallow

353
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Speaker 2: end of the revisionist history research pool, a group of

354
00:22:08,556 --> 00:22:12,396
Speaker 2: students in the front row put their heads together and

355
00:22:12,436 --> 00:22:16,556
Speaker 2: then raise their hands.

356
00:22:16,916 --> 00:22:18,476
Speaker 3: We have a hypothesis.

357
00:22:18,796 --> 00:22:22,596
Speaker 2: That's Adam. Everyone in this front row group had the

358
00:22:22,716 --> 00:22:26,636
Speaker 2: highest privileged score. We handed out twenty four plus. Of

359
00:22:26,756 --> 00:22:29,316
Speaker 2: course they figured it out first. They were the oldest

360
00:22:29,316 --> 00:22:32,756
Speaker 2: students in the class. Next to Adam was Joseph. He

361
00:22:32,876 --> 00:22:34,796
Speaker 2: was wearing a suit and a tie.

362
00:22:35,116 --> 00:22:35,356
Speaker 6: Yeah.

363
00:22:35,836 --> 00:22:36,916
Speaker 4: Yeah, we have a hypothesis.

364
00:22:36,956 --> 00:22:37,796
Speaker 2: So the twenty four.

365
00:22:37,636 --> 00:22:40,156
Speaker 8: Plus is that a significant factor?

366
00:22:40,196 --> 00:22:40,476
Speaker 2: Here?

367
00:22:41,316 --> 00:22:41,836
Speaker 3: Is age?

368
00:22:41,956 --> 00:22:42,996
Speaker 2: Our absolute age?

369
00:22:43,716 --> 00:22:44,996
Speaker 3: Like how old are you those?

370
00:22:45,076 --> 00:22:48,596
Speaker 1: We're all a bunch of old seniors over here, older

371
00:22:48,636 --> 00:22:49,796
Speaker 1: than usual.

372
00:22:50,396 --> 00:22:57,316
Speaker 2: Eureka. Phase one of the experiment was over now phase

373
00:22:57,356 --> 00:23:00,876
Speaker 2: two because I intended to ask them if they wanted

374
00:23:00,876 --> 00:23:15,676
Speaker 2: to do something about their arbitrary privilege. In Australia, they've

375
00:23:15,716 --> 00:23:22,716
Speaker 2: invented something called maturity based Corrective Adjustment Procedures MACCAPS, as

376
00:23:22,756 --> 00:23:26,036
Speaker 2: it's known for provisional use in the sport of swimming.

377
00:23:27,836 --> 00:23:31,636
Speaker 2: I will confess that I am madly in love with

378
00:23:31,676 --> 00:23:34,556
Speaker 2: this idea. It turns out that if you take a

379
00:23:34,596 --> 00:23:37,876
Speaker 2: bunch of measurements of kids and plug them into an equation,

380
00:23:38,476 --> 00:23:42,836
Speaker 2: you can estimate their physical maturity quite accurately. So you

381
00:23:42,916 --> 00:23:45,996
Speaker 2: don't have to rely on chronological age to assess someone's

382
00:23:46,076 --> 00:23:49,716
Speaker 2: level of development. You can do one better and measure

383
00:23:49,996 --> 00:23:51,516
Speaker 2: maturity directly.

384
00:23:52,236 --> 00:23:55,276
Speaker 5: So what these equations do is they fight are in

385
00:23:55,276 --> 00:24:00,556
Speaker 5: indices like height, weight, chronological age and sitting height, and

386
00:24:00,596 --> 00:24:03,956
Speaker 5: they use those fighters to then estimate how far away

387
00:24:04,556 --> 00:24:08,676
Speaker 5: a particular individual is from that point of peak growth.

388
00:24:09,436 --> 00:24:12,876
Speaker 2: That's Stephen Cobley, a professor at the University of Sydney

389
00:24:13,076 --> 00:24:17,076
Speaker 2: who created matcaps along with his colleague Minkel Roman. Here's

390
00:24:17,116 --> 00:24:19,996
Speaker 2: how it works. Imagine we have two fourteen year old

391
00:24:19,996 --> 00:24:23,916
Speaker 2: swimmers competing in a hundred meter freestyle, both with the

392
00:24:23,956 --> 00:24:28,316
Speaker 2: exact same birthday, Joey and Tim. So these academics would

393
00:24:28,356 --> 00:24:32,956
Speaker 2: first calculate the biological maturity status of each swimmer, that is,

394
00:24:33,196 --> 00:24:36,356
Speaker 2: how far each one is from their estimated point of

395
00:24:36,396 --> 00:24:40,756
Speaker 2: peak performance. So let's say, for example, Joey is actually

396
00:24:40,796 --> 00:24:45,356
Speaker 2: twelve months less biologically mature than Tim at this exact moment.

397
00:24:46,236 --> 00:24:50,116
Speaker 2: Then comes the cool part. Cobbley then looks at thousands

398
00:24:50,116 --> 00:24:52,316
Speaker 2: of data points for fourteen year old swimming in a

399
00:24:52,396 --> 00:24:56,036
Speaker 2: hundred meter freestyle and calculates what is twelve months of

400
00:24:56,116 --> 00:24:59,516
Speaker 2: maturity worth on average in terms of swimming time for

401
00:24:59,636 --> 00:25:03,036
Speaker 2: kids competing in that age group. He enters the data

402
00:25:03,156 --> 00:25:07,916
Speaker 2: into the maturation based corrective adjustment procedures algorithm and prestow

403
00:25:09,516 --> 00:25:12,596
Speaker 2: adjusts Joey's time to account for the fact that at

404
00:25:12,636 --> 00:25:16,796
Speaker 2: the moment he raised Tim, he was twelve months behind developmentally.

405
00:25:17,156 --> 00:25:21,196
Speaker 2: An adolescent swim meet in Cobley's ideal universe has two

406
00:25:21,276 --> 00:25:25,516
Speaker 2: sets of results, the raw results and then the maturity

407
00:25:25,636 --> 00:25:26,716
Speaker 2: adjusted results.

408
00:25:27,716 --> 00:25:31,436
Speaker 5: What you're doing is you're effectively lowering the time of

409
00:25:31,516 --> 00:25:34,636
Speaker 5: the folks who are slightly behind in terms of their

410
00:25:34,796 --> 00:25:35,876
Speaker 5: maturity status.

411
00:25:36,676 --> 00:25:39,716
Speaker 2: Cobbley did a test run of the maccap's algorithm on

412
00:25:39,796 --> 00:25:44,116
Speaker 2: a sample of seven hundred Australian swimmers, all boys competing

413
00:25:44,156 --> 00:25:47,716
Speaker 2: in one hundred meter freestyle. The first thing he discovers

414
00:25:47,836 --> 00:25:50,396
Speaker 2: is similar to what we found a pen. Among the

415
00:25:50,436 --> 00:25:54,036
Speaker 2: top twenty five percent of all adolescent swimmers, there were

416
00:25:54,156 --> 00:25:59,636
Speaker 2: no late maturing boys, none, zero, which is an astonishing fact.

417
00:26:00,236 --> 00:26:03,956
Speaker 2: Australia is a country that takes swimming as seriously as

418
00:26:03,996 --> 00:26:08,116
Speaker 2: Canada takes hockey, and they have basically decided to banish

419
00:26:08,276 --> 00:26:12,196
Speaker 2: a big group of young swimmers from consideration just because

420
00:26:12,236 --> 00:26:16,676
Speaker 2: their talent happens not to appear soon enough. When you

421
00:26:16,676 --> 00:26:19,996
Speaker 2: looked at these seven hundred swimmers, in some sense, the

422
00:26:20,116 --> 00:26:23,916
Speaker 2: damage has already been done. We've already chased away the

423
00:26:23,956 --> 00:26:29,396
Speaker 2: slow developers. They've quit, they've got discouraged. They they thought

424
00:26:29,436 --> 00:26:34,356
Speaker 2: they were bad swimmers. They didn't realize they were simply behind. Yeah,

425
00:26:35,036 --> 00:26:38,076
Speaker 2: so what happens when you run everyone's race times through

426
00:26:38,076 --> 00:26:42,836
Speaker 2: the Mattcaps algorithm, adjusting for maturity, the order of finish

427
00:26:43,116 --> 00:26:46,876
Speaker 2: in every race changes. They're really talented swimmers who just

428
00:26:46,916 --> 00:26:49,676
Speaker 2: happen to be slower to mature now have a chance.

429
00:26:50,276 --> 00:26:52,396
Speaker 2: They used to be lost to the system. Now you

430
00:26:52,396 --> 00:26:54,756
Speaker 2: can tell who they are. Now you can go up

431
00:26:54,756 --> 00:26:57,036
Speaker 2: to young Joey and say, I know you didn't make

432
00:26:57,036 --> 00:26:59,716
Speaker 2: the final, but take a look at your matt CAP's time.

433
00:27:00,076 --> 00:27:02,916
Speaker 2: You might be the best swimmer out there. What's the

434
00:27:02,956 --> 00:27:04,476
Speaker 2: most somebody moved up?

435
00:27:05,516 --> 00:27:08,956
Speaker 5: But we've seen large percentages, We've certainly seen big changes

436
00:27:08,956 --> 00:27:13,556
Speaker 5: in ranks. So if we've got cases for events where

437
00:27:14,036 --> 00:27:17,756
Speaker 5: someone who was outside let's say the top twenty, suddenly

438
00:27:17,836 --> 00:27:18,596
Speaker 5: was in the top three.

439
00:27:19,676 --> 00:27:22,596
Speaker 2: I read your paper and the first that I had was,

440
00:27:23,236 --> 00:27:28,156
Speaker 2: oh wow, this belongs in the classroom. Right when you

441
00:27:28,196 --> 00:27:31,756
Speaker 2: identify who gets into special gifted and talented programs, or

442
00:27:31,796 --> 00:27:34,796
Speaker 2: when you decide who just isn't smart enough for when

443
00:27:34,836 --> 00:27:37,996
Speaker 2: you look at who you discourage and who you encourage,

444
00:27:38,236 --> 00:27:40,396
Speaker 2: you've got to be making the same mistake.

445
00:27:40,516 --> 00:27:45,036
Speaker 5: Right, Yeah, I think so. I think the cautionary bit

446
00:27:45,076 --> 00:27:47,836
Speaker 5: that we have to remember is it's that old question

447
00:27:47,876 --> 00:27:50,196
Speaker 5: of yeah, but how far do we go? So if

448
00:27:50,196 --> 00:27:52,556
Speaker 5: we're going to factor, if we are going to factor

449
00:27:52,636 --> 00:27:57,596
Speaker 5: relative agent in education or biological development in education adjustments,

450
00:27:57,956 --> 00:28:00,036
Speaker 5: shouldn't we be factoring in other things that we know

451
00:28:00,116 --> 00:28:00,916
Speaker 5: are influential?

452
00:28:01,436 --> 00:28:08,276
Speaker 2: Absolutely? Why wouldn't we, Well, exactly why wouldn't we If

453
00:28:08,276 --> 00:28:11,836
Speaker 2: we've developed a better way of identifying talent. Why wouldn't

454
00:28:11,876 --> 00:28:17,956
Speaker 2: we want to use it everywhere? Back at Wharton, I

455
00:28:18,156 --> 00:28:22,036
Speaker 2: climbed up on my soapbox. I talked about how maccaps

456
00:28:22,156 --> 00:28:25,876
Speaker 2: had freed swimmers from the tyranny of birthdays, communicated my

457
00:28:26,276 --> 00:28:30,436
Speaker 2: enthusiasm for bringing the Australian Revolution to the shores of

458
00:28:30,436 --> 00:28:33,196
Speaker 2: the United States. So in Australia they started to do this.

459
00:28:33,476 --> 00:28:36,316
Speaker 2: Eleven year olds are all swimming the one hundred meter freestyle.

460
00:28:37,476 --> 00:28:40,956
Speaker 2: We've got twelve kids. We have, you know, an order

461
00:28:40,956 --> 00:28:43,876
Speaker 2: of finish. Then they run the times through an algorithm

462
00:28:43,996 --> 00:28:48,596
Speaker 2: and have a new Now, would you feel comfortable with

463
00:28:48,796 --> 00:28:51,156
Speaker 2: all of your if you go back to your k

464
00:28:51,236 --> 00:28:54,236
Speaker 2: through twelve experiences? Would you feel comfortable if they ran

465
00:28:54,636 --> 00:28:58,316
Speaker 2: all of your test scores through an age correction algorithm?

466
00:28:58,756 --> 00:29:02,476
Speaker 2: Around the room I saw young people of promise, focused,

467
00:29:02,796 --> 00:29:06,756
Speaker 2: eager they would be my disciples. I was so full

468
00:29:06,796 --> 00:29:10,996
Speaker 2: of excitement. I put the question to a vote, yes

469
00:29:11,076 --> 00:29:14,676
Speaker 2: or no, I say to a shorthands who likes the idea.

470
00:29:16,356 --> 00:29:19,476
Speaker 2: There was a great stirring and wrestling. My heart leapt

471
00:29:19,476 --> 00:29:21,956
Speaker 2: into my throat. I thought I had brought the birthday

472
00:29:21,996 --> 00:29:25,356
Speaker 2: rights revolution into the heart of the lion's den. I

473
00:29:25,396 --> 00:29:32,436
Speaker 2: looked up, looked around, and nothing, no roar of support,

474
00:29:33,356 --> 00:29:40,036
Speaker 2: only a long, cold silence. I've never seen less enthusiasm

475
00:29:40,156 --> 00:29:42,556
Speaker 2: for a great idea in my life. Wait, what is

476
00:29:42,596 --> 00:29:46,716
Speaker 2: the matter with you guys? A young man spoke up first.

477
00:29:47,276 --> 00:29:51,556
Speaker 10: Well, it's like, be completely honest. It's to be selfish

478
00:29:51,636 --> 00:29:53,716
Speaker 10: about it. It would probably have heard our chances of

479
00:29:53,716 --> 00:29:57,236
Speaker 10: being right here in this room because I'm old. For

480
00:29:57,356 --> 00:29:59,996
Speaker 10: my grade, I did well my standardized test scores. Maybe

481
00:29:59,996 --> 00:30:02,116
Speaker 10: if they readjusted it, I would be more in the mediate.

482
00:30:02,796 --> 00:30:05,076
Speaker 2: He was a twenty two year old senior in college.

483
00:30:05,996 --> 00:30:08,236
Speaker 10: So selfishly I would say, no, it's not a good

484
00:30:08,236 --> 00:30:10,156
Speaker 10: idea from a societal standpoint.

485
00:30:10,916 --> 00:30:12,116
Speaker 2: Perhaps, so you're being honest.

486
00:30:12,156 --> 00:30:14,836
Speaker 10: I'm being honest. Yeah, it's like saying, uh, you know,

487
00:30:15,196 --> 00:30:17,596
Speaker 10: legacy admissions or something like that. My father went to

488
00:30:17,596 --> 00:30:18,676
Speaker 10: PEN and my mother went to pen.

489
00:30:18,996 --> 00:30:21,636
Speaker 2: Oh you're drowning. I'm drowning.

490
00:30:21,796 --> 00:30:23,796
Speaker 10: Yes, if we get rid of this, you know, But

491
00:30:23,876 --> 00:30:24,796
Speaker 10: I'm just going to be honest.

492
00:30:27,316 --> 00:30:31,996
Speaker 2: It's fantastic. Who else wants to Then Mateo raises his hand.

493
00:30:32,356 --> 00:30:35,316
Speaker 2: He is an eighteen on his sticker, an age privilege

494
00:30:35,316 --> 00:30:37,596
Speaker 2: advantage of a year and a half.

495
00:30:38,716 --> 00:30:41,036
Speaker 4: And I think that's a poor idea because it assumes

496
00:30:41,036 --> 00:30:43,556
Speaker 4: that everyone who is older is like always going to

497
00:30:43,556 --> 00:30:45,236
Speaker 4: be smarter, and I want whose owner is always going

498
00:30:45,276 --> 00:30:47,676
Speaker 4: to be less smart. I've seen some pretty old people

499
00:30:47,716 --> 00:30:48,636
Speaker 4: do some terrible things.

500
00:30:49,476 --> 00:30:53,116
Speaker 2: No, no, no, BUTTAO does not what it does well, it's neutral.

501
00:30:53,596 --> 00:30:55,796
Speaker 2: It just adjusts for the age, Grady.

502
00:30:55,876 --> 00:30:57,356
Speaker 4: I want my score to be my score.

503
00:30:57,516 --> 00:30:59,156
Speaker 2: But wait, wait, I want to I want to. Can

504
00:30:59,156 --> 00:31:01,196
Speaker 2: I just harp? You said you want your score to

505
00:31:01,196 --> 00:31:01,836
Speaker 2: be your score?

506
00:31:02,036 --> 00:31:02,436
Speaker 6: Yeah?

507
00:31:02,476 --> 00:31:05,716
Speaker 2: But why is? Why is an adjusted score a score

508
00:31:05,716 --> 00:31:09,836
Speaker 2: that accounts for your degree of maturity somehow less characteristic

509
00:31:09,836 --> 00:31:12,236
Speaker 2: of who you are than an unadjusted score. I would

510
00:31:12,236 --> 00:31:16,196
Speaker 2: have thought the opposite. A score that doesn't include information

511
00:31:16,276 --> 00:31:18,156
Speaker 2: on your level of maturity would seem to be more

512
00:31:18,236 --> 00:31:19,476
Speaker 2: artificial than one that does.

513
00:31:20,196 --> 00:31:21,916
Speaker 4: I don't know, I guess i'd have I would want

514
00:31:21,916 --> 00:31:24,276
Speaker 4: to look at the algorithm before I made an actual judgment,

515
00:31:24,596 --> 00:31:28,796
Speaker 4: because I'd be surprised if I was okay with everything theoretically,

516
00:31:28,836 --> 00:31:30,636
Speaker 4: that everything that the algorithm would say.

517
00:31:31,356 --> 00:31:34,636
Speaker 2: The students stood up, one by one, using their prodigious

518
00:31:34,676 --> 00:31:38,476
Speaker 2: powers of analysis and imagination to come up with one

519
00:31:38,476 --> 00:31:42,396
Speaker 2: objection after another. Why do you think you guys are

520
00:31:42,436 --> 00:31:45,836
Speaker 2: so hostile to it. Two attempts to remedy the situation.

521
00:31:47,436 --> 00:31:49,276
Speaker 8: My fear with the algorithm is that it could be gamed.

522
00:31:49,396 --> 00:31:52,476
Speaker 8: So if this were implemented where we know that if

523
00:31:52,476 --> 00:31:54,436
Speaker 8: you're younger, you get say one hundred point bump in

524
00:31:54,436 --> 00:31:58,316
Speaker 8: the SAT or are viewed more favorably throughout your educational career,

525
00:31:58,676 --> 00:32:00,796
Speaker 8: then we're probably sending our kids off to kindergarten at four,

526
00:32:01,676 --> 00:32:03,996
Speaker 8: or we're planning whenever we have our kids, looking at

527
00:32:03,996 --> 00:32:05,796
Speaker 8: whever the cutoff date is for kindergarten, you know, in

528
00:32:05,796 --> 00:32:08,196
Speaker 8: September maybe, and saying all right, we're going to reproduce

529
00:32:08,276 --> 00:32:13,396
Speaker 8: nine months before that in December or January.

530
00:32:14,236 --> 00:32:18,076
Speaker 2: And the current system that's being gamed. We're responding to

531
00:32:18,116 --> 00:32:19,436
Speaker 2: the gaming, are we not?

532
00:32:20,516 --> 00:32:25,236
Speaker 8: Yeah, so I guess I'm the fears that the algrum regained.

533
00:32:26,436 --> 00:32:27,916
Speaker 8: And yes, exactly.

534
00:32:32,356 --> 00:32:34,756
Speaker 2: I put my hand on the table to steady myself.

535
00:32:35,116 --> 00:32:38,356
Speaker 2: My head was spinning. These were the children of outliers,

536
00:32:38,916 --> 00:32:41,516
Speaker 2: children raised according to the rules of a game I

537
00:32:41,636 --> 00:32:45,516
Speaker 2: kind of helped set in motion. And now the consensus

538
00:32:45,516 --> 00:32:49,636
Speaker 2: among seventy five elderly ivy leaguers was that the system

539
00:32:49,676 --> 00:32:53,876
Speaker 2: should remain rigged in favor of the elderly. The apple

540
00:32:53,956 --> 00:32:58,476
Speaker 2: cart must remain upright with the shiniest and oldest apples

541
00:32:58,836 --> 00:33:04,516
Speaker 2: on the top. Now do I blame them, No, I don't.

542
00:33:05,116 --> 00:33:07,516
Speaker 2: This is what happens when we give up on fairness

543
00:33:07,836 --> 00:33:12,916
Speaker 2: as an essential principle. All that remains is cynicism. The

544
00:33:12,956 --> 00:33:15,436
Speaker 2: students a PEN do not see the point of changing

545
00:33:15,436 --> 00:33:18,516
Speaker 2: the system because their parents did not see the point

546
00:33:18,516 --> 00:33:21,236
Speaker 2: of changing the system, and their parents didn't see the

547
00:33:21,276 --> 00:33:25,116
Speaker 2: point because the schools didn't see the point. And the schools,

548
00:33:25,156 --> 00:33:28,436
Speaker 2: for goodness sake, can't even rise from the slumber of

549
00:33:28,476 --> 00:33:31,116
Speaker 2: their indifference to see that it makes no sense to

550
00:33:31,156 --> 00:33:35,516
Speaker 2: give everyone an assessment test on the same day we

551
00:33:35,676 --> 00:33:40,716
Speaker 2: gain the things that we've given up on. I try

552
00:33:40,796 --> 00:33:44,116
Speaker 2: my best in Outliers, but I subtitled the book the

553
00:33:44,156 --> 00:33:47,036
Speaker 2: Story of Success, And if I learned anything from that

554
00:33:47,156 --> 00:33:49,916
Speaker 2: afternoon at PEN, it's that we want to think about

555
00:33:49,956 --> 00:33:54,716
Speaker 2: success as a word to describe ourselves, our own progress.

556
00:33:55,436 --> 00:33:58,836
Speaker 2: But it's not really people who are successful. It's the

557
00:33:58,916 --> 00:34:03,756
Speaker 2: systems around us. Great students and great hockey players come

558
00:34:03,796 --> 00:34:07,156
Speaker 2: from great teams and great classrooms. And if you want

559
00:34:07,156 --> 00:34:10,956
Speaker 2: to judge the success of those teams in classrooms start

560
00:34:10,996 --> 00:34:15,956
Speaker 2: by looking at their composition, like when was everyone born?

561
00:34:17,556 --> 00:34:21,476
Speaker 2: And if we can't get that one right, God help

562
00:34:21,556 --> 00:34:29,516
Speaker 2: us with everything else. All right, Thank you guys. I

563
00:34:29,556 --> 00:34:32,876
Speaker 2: hope this has all been fun. I hope this makes

564
00:34:32,876 --> 00:34:35,436
Speaker 2: you feel free to wear your numbers for the balance

565
00:34:35,476 --> 00:34:43,276
Speaker 2: of the school year. Revisionist History is produced by Eloise Linton,

566
00:34:43,596 --> 00:34:48,316
Speaker 2: Leeman Gestu, and Jacob Smith, with Tolly Emlin and Harrison VJ. Choi.

567
00:34:48,836 --> 00:34:52,996
Speaker 2: Our editor is Julia Barton. Our executive producer is Mia Label.

568
00:34:53,476 --> 00:34:57,356
Speaker 2: Original scoring by Luis Kara, mastering by Flon Williams, and

569
00:34:57,476 --> 00:35:02,476
Speaker 2: engineering by Nina Lawrence. Fact checking by Keishaw Williams. Special

570
00:35:02,476 --> 00:35:06,396
Speaker 2: thanks to somon ohod Kahn for production help on this episode.

571
00:35:07,196 --> 00:35:17,676
Speaker 2: I'm Malcolm glava Hey Revision's History listeners. I'm capping off

572
00:35:17,676 --> 00:35:20,676
Speaker 2: this episode with a preview of a new Pushkin show

573
00:35:21,036 --> 00:35:23,796
Speaker 2: that really has me hooked. It's called Death of an Artist.

574
00:35:24,356 --> 00:35:26,316
Speaker 2: Death of an Artist has all the elements of a

575
00:35:26,356 --> 00:35:31,516
Speaker 2: gripping story, a suspicious death, tumultuous relationship, a murder trial,

576
00:35:31,636 --> 00:35:35,116
Speaker 2: questions of morality, feminism, power and balance, and a divided

577
00:35:35,196 --> 00:35:38,436
Speaker 2: art world. On September eighth, nineteen eighty five, up and

578
00:35:38,476 --> 00:35:42,516
Speaker 2: coming artist Anna Mendietta fell from the thirty fourth floor

579
00:35:42,516 --> 00:35:46,636
Speaker 2: window of her husband and famous sculptor Karl Andre's apartment.

580
00:35:47,356 --> 00:35:52,476
Speaker 2: Host Helen Molesworth asks was Karl Andre involved? You'll revisit

581
00:35:52,556 --> 00:35:55,676
Speaker 2: on his untimely death, the trial that followed, and both

582
00:35:55,676 --> 00:36:00,516
Speaker 2: the protests and silence that have followed this story ever since. Okay,

583
00:36:00,636 --> 00:36:03,396
Speaker 2: here comes a sneak peek. You can follow the story

584
00:36:03,556 --> 00:36:06,876
Speaker 2: by searching for death of an Artist wherever you get

585
00:36:06,916 --> 00:36:08,036
Speaker 2: your podcasts.

586
00:36:08,716 --> 00:36:13,956
Speaker 1: It's in a nineteen seventy three Iowa city. There's a

587
00:36:13,996 --> 00:36:16,956
Speaker 1: damp chill in the air. We are on a sort

588
00:36:16,956 --> 00:36:20,156
Speaker 1: of shabby block in front of a brick apartment building

589
00:36:20,236 --> 00:36:22,436
Speaker 1: with a white door in need of a paint job

590
00:36:22,676 --> 00:36:28,076
Speaker 1: and a storefront window with its blind strawn shut. The

591
00:36:28,156 --> 00:36:32,116
Speaker 1: sidewalk just in front of the door is covered in blood,

592
00:36:33,116 --> 00:36:35,876
Speaker 1: and it looks like the blood might be seeping out

593
00:36:35,956 --> 00:36:40,756
Speaker 1: from under the door jam. It's a busy weekday and

594
00:36:40,796 --> 00:36:43,836
Speaker 1: as pedestrians pass the puddle of blood, they notice it

595
00:36:43,956 --> 00:36:47,836
Speaker 1: and casually step around it. Eventually, a man in a

596
00:36:47,876 --> 00:36:52,076
Speaker 1: green and black plaid jacket pauses and looks around as

597
00:36:52,076 --> 00:36:57,036
Speaker 1: if looking for an explanation. When none comes, he walks away.

598
00:36:57,916 --> 00:37:00,836
Speaker 1: Then a well dressed white woman uses her umbrella to

599
00:37:00,876 --> 00:37:03,756
Speaker 1: poke at the bloody puddle, but after a moment or

600
00:37:03,796 --> 00:37:09,076
Speaker 1: two of inspection, she also walks away. Finally, an old,

601
00:37:09,116 --> 00:37:13,436
Speaker 1: older gentleman emerges from a nearby storefront and silently cleans

602
00:37:13,516 --> 00:37:16,956
Speaker 1: up the mess, and the evidence of whatever happened is

603
00:37:16,956 --> 00:37:21,436
Speaker 1: suddenly gone, and with it disappears any account of whose

604
00:37:21,476 --> 00:37:29,036
Speaker 1: blood was spilled and how. The whole scene is being

605
00:37:29,076 --> 00:37:33,276
Speaker 1: captured by two young women in their twenties, sisters who

606
00:37:33,356 --> 00:37:36,876
Speaker 1: sit in an old car park nearby. One of them

607
00:37:36,916 --> 00:37:39,756
Speaker 1: holds a super eight camera, the kind you'd make home

608
00:37:39,796 --> 00:37:43,796
Speaker 1: movies with back then the other snaps photos with a

609
00:37:43,836 --> 00:37:49,036
Speaker 1: thirty five millimeter camera. They are Anna and Raklein Mendietta,

610
00:37:49,596 --> 00:37:53,676
Speaker 1: Cuban refugees who landed in this unlikely place as children

611
00:37:58,396 --> 00:38:01,396
Speaker 1: in nineteen seventy three. Anna was a first year MFA

612
00:38:01,476 --> 00:38:06,596
Speaker 1: student at the University of Iowa. She was funny, loud, outrageous,

613
00:38:06,876 --> 00:38:10,156
Speaker 1: and had to take no prisoner's vibe and in the

614
00:38:10,196 --> 00:38:13,316
Speaker 1: way of sisters, she had roped directlyan into helping her

615
00:38:13,356 --> 00:38:16,556
Speaker 1: make a new piece, and like many works on a maid.

616
00:38:17,076 --> 00:38:20,036
Speaker 1: It would come to seem tragically prophetic in the wake

617
00:38:20,076 --> 00:38:20,636
Speaker 1: of her death.

618
00:38:22,556 --> 00:38:29,316
Speaker 9: She basically staged what looked like the remnants of, you know,

619
00:38:29,356 --> 00:38:32,516
Speaker 9: physical violence with what looked like blood in the doorway

620
00:38:32,596 --> 00:38:35,516
Speaker 9: of a building, and I thought it was extremely powerful

621
00:38:36,156 --> 00:38:39,236
Speaker 9: for a very young artist to be doing that, and

622
00:38:39,316 --> 00:38:42,676
Speaker 9: to be doing it in this small, largely white town

623
00:38:42,756 --> 00:38:45,156
Speaker 9: of Iowa City was fascinating to me.

624
00:38:46,196 --> 00:38:49,076
Speaker 1: That's Connie Butler, one of the many curators who would

625
00:38:49,116 --> 00:38:51,876
Speaker 1: come to admire and study on a Mendietta's work in

626
00:38:51,916 --> 00:38:55,796
Speaker 1: the decades that followed. The photos in film the Mendieta

627
00:38:55,876 --> 00:38:58,916
Speaker 1: sisters took that day would ultimately become a work of

628
00:38:59,036 --> 00:39:01,556
Speaker 1: art called Moffat Building Piece.

629
00:39:02,476 --> 00:39:04,676
Speaker 9: The fact that it still exists only in these little

630
00:39:04,716 --> 00:39:07,636
Speaker 9: thirty five millimeters slides, which you know you have to

631
00:39:07,676 --> 00:39:11,196
Speaker 9: get very close to with a loop, and it's a

632
00:39:11,276 --> 00:39:15,316
Speaker 9: very intimate way of viewing these things. You know, that

633
00:39:15,356 --> 00:39:17,636
Speaker 9: implicates you as a viewer too, almost as if you

634
00:39:17,676 --> 00:39:19,356
Speaker 9: are yourself looking at a crime scene.

635
00:39:20,556 --> 00:39:23,716
Speaker 1: Anna's interest in blood wasn't only meant to shock. She

636
00:39:23,836 --> 00:39:27,756
Speaker 1: was keenly aware of violence and injustice. When she made

637
00:39:27,796 --> 00:39:30,796
Speaker 1: them off At Building Piece. She was investigating her own

638
00:39:30,876 --> 00:39:35,156
Speaker 1: community's reaction to a brutal crime, a rape and murder

639
00:39:35,156 --> 00:39:38,596
Speaker 1: that had happened on campus a few months before. Here's

640
00:39:38,636 --> 00:39:40,436
Speaker 1: how she explained her inspiration.

641
00:39:41,476 --> 00:39:44,716
Speaker 11: A young woman was killed, raped and killed at Iowa

642
00:39:45,156 --> 00:39:47,876
Speaker 11: in one of the dorms, and it just really freaked

643
00:39:47,916 --> 00:39:51,556
Speaker 11: me out. So I did several rape performances type things

644
00:39:51,716 --> 00:39:55,516
Speaker 11: at that time, using my own body. I did something

645
00:39:55,556 --> 00:39:58,996
Speaker 11: I believe in and that I felt I had to do.

646
00:39:59,756 --> 00:40:03,436
Speaker 1: That's not actually Anna Mendieta's voice you're hearing. That was

647
00:40:03,476 --> 00:40:07,196
Speaker 1: Tanya Burghera, another artist from Cuba who you'll hear from

648
00:40:07,236 --> 00:40:16,276
Speaker 1: more later. Anna Mendieta's question was could you make art

649
00:40:16,436 --> 00:40:20,676
Speaker 1: about something so awful? And she used blood, not paint.

650
00:40:21,236 --> 00:40:24,596
Speaker 1: Blood is the most essential substance of life. Could it

651
00:40:24,676 --> 00:40:28,396
Speaker 1: jolt people out of their daily routines? Could blood make

652
00:40:28,436 --> 00:40:32,716
Speaker 1: people pay attention? She didn't know it yet, but the

653
00:40:32,756 --> 00:40:35,956
Speaker 1: Moffat Building piece was about to be her first major artwork,

654
00:40:36,716 --> 00:40:40,436
Speaker 1: and in a circular way, that's kind of terrifying. The

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Speaker 1: question she asked about how we react when we encounter

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00:40:44,836 --> 00:40:48,916
Speaker 1: the residue of violence. This question would haunt all of

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Speaker 1: us after she died. I'm your host Helen Moulesworth and

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Speaker 1: from Pushkin Industries, Something Else and Sony Music Entertainment. This

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Speaker 1: is Death of an Artist, Episode one. The haunting for

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00:41:18,556 --> 00:41:21,476
Speaker 1: my entire professional life. I've been a member of something

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Speaker 1: called the art world, an exclusive network of artists, gallery dealers, curators, collectors,

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00:41:28,196 --> 00:41:32,156
Speaker 1: and philanthropists for two decades. I was lucky enough to

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Speaker 1: be a museum curator, making me one of a small

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Speaker 1: group of cultural insiders who determine what art we see

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Speaker 1: and how we talk about it. In the museum world

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Speaker 1: and in art history, there are a lot of unspoken

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00:41:45,676 --> 00:41:48,676
Speaker 1: rules about what you can say publicly and what is

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00:41:48,676 --> 00:41:52,836
Speaker 1: supposed to stay private. It turns out I wasn't that

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Speaker 1: good at sticking to the script, and I guess I'm

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00:41:56,156 --> 00:41:58,596
Speaker 1: still not good at it, because I'm going to tell

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Speaker 1: you Anna Mendietta's whole story, all the way to its

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00:42:02,756 --> 00:42:09,796
Speaker 1: shocking and troubling end, and much to my surprise, I

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Speaker 1: discovered it's a story many of my colleagues in the

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Speaker 1: art world would prefer I didn't tell at first, blush.

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Speaker 1: It seemed like people didn't want me to talk about

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00:42:23,036 --> 00:42:25,556
Speaker 1: it because of who else is part of that story.

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Speaker 1: Anna's husband, the famous sculptor Carl Andre. He is one

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Speaker 1: of the so called fathers of minimalism, a cultural hero

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Speaker 1: to many are revered artists with lots of connections, and

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Speaker 1: he was a suspect in Anna's death. Even though Carl

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00:42:46,156 --> 00:42:49,836
Speaker 1: Andrea and Anna Mendieta were a highly visible art world couple,

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Speaker 1: even though something terrible happened between them the night she died,

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Speaker 1: you will not read about it on a museum wall

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Speaker 1: label or in most art history textbooks reviews of their

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Speaker 1: exhibitions tend to take care of it in a sentence

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Speaker 1: or two. You would not know that Mendieta's death divided

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Speaker 1: the art world in nineteen eight, and in many ways

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00:43:11,916 --> 00:43:19,556
Speaker 1: still does. I'm not the first person to try and

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Speaker 1: tell this story. In fact, many of the voices you'll

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Speaker 1: hear in this show are from interviews conducted by investigative

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Speaker 1: journalist Robert Katz. He published a book in nineteen ninety

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Speaker 1: that remains the most comprehensive look into this art world tragedy.

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Speaker 1: He spoke with dozens of Anna and Carl's friends in

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Speaker 1: noisy restaurants, in parks, in busy offices, and you'll hear

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Speaker 1: the voices of some art world insiders on these tapes

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Speaker 1: who have since decided not to talk. Most folks don't

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Speaker 1: want to discuss what happened that night. They don't want

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Speaker 1: to talk about what the ramifications of that night were

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00:43:56,716 --> 00:43:59,916
Speaker 1: on the art world. They don't want to contemplate what

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Speaker 1: it means when a community is torn apart by violence,

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00:44:03,996 --> 00:44:06,796
Speaker 1: and they don't want to discuss whether or not justice

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00:44:06,916 --> 00:44:11,596
Speaker 1: has been served. These different folks not talking for all

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00:44:11,676 --> 00:44:15,116
Speaker 1: of their different reasons means that a veil of silence

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00:44:15,196 --> 00:44:19,996
Speaker 1: started to fall over this project, and I can't lie.

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Speaker 1: The more silence we encountered, the more sad and frustrated

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Speaker 1: I became. And the more silence we encountered, the more

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Speaker 1: I wanted to talk,