3 kinds of bias that shape your worldview | J. Marshall Shepherd

309,332 views ・ 2019-01-02

TED


μ•„λž˜ μ˜λ¬Έμžλ§‰μ„ λ”λΈ”ν΄λ¦­ν•˜μ‹œλ©΄ μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€.

λ²ˆμ—­: Alicia Chong κ²€ν† : Jihyeon J. Kim
00:12
I'm a meteorologist by degree,
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ν•™μœ„λ‘œ 따지면 μ €λŠ” κΈ°μƒν•™μžμž…λ‹ˆλ‹€
00:14
I have a bachelor's, master's and PhD in physical meteorology,
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μ €λŠ” 물리 기상학에 학사, 석사, 박사 ν•™μœ„κ°€ μžˆμŠ΅λ‹ˆλ‹€
00:17
so I'm a meteorologist, card carrying.
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μ €λŠ” 정식 κΈ°μƒν•™μžμž…λ‹ˆλ‹€
00:20
And so with that comes four questions, always.
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κ·Έλž˜μ„œ λ„€ 가지 μ§ˆλ¬Έλ“€μ΄ 항상 λ”°λΌλ‹€λ‹™λ‹ˆλ‹€
00:25
This is one prediction I will always get right.
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이건 μ œκ°€ 항상 λ§žμΆ”λŠ” 예츑 쀑 ν•˜λ‚˜μž…λ‹ˆλ‹€
00:27
(Laughter)
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(μ›ƒμŒ)
00:29
And those questions are,
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그리고 κ·Έ μ§ˆλ¬Έλ“€μ€ λ‹€μŒκ³Ό κ°™μ•„μš”.
00:31
"Marshall, what channel are you on?"
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"λ§ˆμƒ¬, 당신은 μ–΄λŠ 채널에 λ‚˜μ˜€λ‚˜μš”?"
00:34
(Laughter)
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(μ›ƒμŒ)
00:36
"Dr. Shepherd, what's the weather going to be tomorrow?"
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"μ…°νΌλ“œ λ°•μ‚¬λ‹˜, 내일 λ‚ μ”¨λŠ” μ–΄λ–€κ°€μš”?"
00:38
(Laughter)
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(μ›ƒμŒ)
00:39
And oh, I love this one:
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그리고 μ „ 이 μ§ˆλ¬Έμ„ μ’‹μ•„ν•˜λŠ”λ°μš”.
00:41
"My daughter is getting married next September, it's an outdoor wedding.
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"제 딸이 λ‚΄λ…„ 9월에 κ²°ν˜Όμ„ ν•  건데, μ•Όμ™Έ κ²°ν˜Όμ‹μ΄μ—μš”.
00:45
Is it going to rain?"
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λΉ„κ°€ μ˜¬κΉŒμš”?"
00:46
(Laughter)
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(μ›ƒμŒ)
00:47
Not kidding, I get those, and I don't know the answer to that,
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농담이 μ•„λ‹ˆλΌ μ €λŸ° μ§ˆλ¬Έλ“€μ„ λ°›μ§€λ§Œ 저도 정닡을 λͺ¨λ¦…λ‹ˆλ‹€.
00:50
the science isn't there.
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과학적인 질문이 μ•„λ‹ˆλ‹ˆκΉŒμš”.
00:53
But the one I get a lot these days is,
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ν•˜μ§€λ§Œ μ œκ°€ μš”μ¦˜ λ“€μ–΄μ„œ 많이 λ°›λŠ” μ§ˆλ¬Έμ€
00:56
"Dr. Shepherd, do you believe in climate change?"
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"μ…°νΌλ“œ λ°•μ‚¬λ‹˜μ€ κΈ°ν›„ λ³€ν™” ν˜„μƒμ„ λ―ΏμœΌμ‹œλ‚˜μš”?"
01:01
"Do you believe in global warming?"
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"지ꡬ μ˜¨λ‚œν™” ν˜„μƒμ„ λ―ΏμœΌμ‹œλ‚˜μš”?"
01:04
Now, I have to gather myself every time I get that question.
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μ €λŸ° μ§ˆλ¬Έλ“€μ„ 받을 λ•Œλ§ˆλ‹€ 마음의 μ€€λΉ„λ₯Ό λ‹¨λ‹¨νžˆ ν•΄μ•Όν•©λ‹ˆλ‹€.
01:08
Because it's an ill-posed question --
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질문이 νƒ€λ‹Ήν•˜μ§€ μ•ŠμœΌλ‹ˆκΉŒμš”.
01:10
science isn't a belief system.
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과학은 신념체계가 μ•„λ‹ˆμ—μš”.
01:12
My son, he's 10 -- he believes in the tooth fairy.
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제 10μ‚΄ 짜리 아듀은 μ΄λΉ¨μš”μ •μ΄ μžˆλ‹€κ³  λ―ΏμŠ΅λ‹ˆλ‹€.
01:16
And he needs to get over that, because I'm losing dollars, fast.
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제 돈이 κΈ‰κ²©νžˆ 쀄고 μžˆμ–΄μ„œ 아듀이 κ·Έ 싀체λ₯Ό μ•Œμ•„μ•Ό ν• ν…λ°μš”.
01:20
(Laughter)
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(μ›ƒμŒ)
01:22
But he believes in the tooth fairy.
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근데 제 아듀은 μ΄λΉ¨μš”μ •μ˜ 쑴재λ₯Ό λ―ΏμŠ΅λ‹ˆλ‹€.
01:24
But consider this.
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ν•˜μ§€λ§Œ 이것을 ν•œλ²ˆ μƒκ°ν•΄λ³΄μ„Έμš”.
01:27
Bank of America building, there, in Atlanta.
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이 사진은 μ• ν‹€λžœνƒ€μ˜ λ―Έκ΅­ μ€ν–‰μž…λ‹ˆλ‹€.
01:29
You never hear anyone say,
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μš°λ¦¬λŠ” 이런 μ§ˆλ¬Έμ„ λ“€μ–΄λ³Έ 적이 없을 κ±΄λ°μš”.
01:32
"Do you believe, if you go to the top of that building
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"당신은 μ € λΉŒλ”© κΌ­λŒ€κΈ°μ— κ°€μ„œ
01:35
and throw a ball off, it's going to fall?"
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곡을 λ˜μ§€λ©΄ λ–¨μ–΄μ§ˆ 것을 λ―ΏμŠ΅λ‹ˆκΉŒ?"
01:37
You never hear that, because gravity is a thing.
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μš°λ¦¬λŠ” 이런 μ§ˆλ¬Έμ„ 받지 μ•ŠλŠ” 게 쀑λ ₯μ΄λΌλŠ” 것이 μ‘΄μž¬ν•˜κΈ° λ•Œλ¬Έμ΄μ£ .
01:42
So why don't we hear the question,
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그럼 μš°λ¦¬λŠ” μ™œ 이 μ§ˆλ¬Έμ„ 듀어보지 λͺ»ν–ˆμ„κΉŒμš”.
01:44
"Do you believe in gravity?"
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"당신은 쀑λ ₯의 쑴재λ₯Ό λ―ΏμŠ΅λ‹ˆκΉŒ?"
01:46
But of course, we hear the question,
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ν•˜μ§€λ§Œ λ‹Ήμ—°νžˆ μš°λ¦¬λŠ” 이런 μ§ˆλ¬Έμ„ λ“€μ–΄μš”.
01:48
"Do you believe in global warming?"
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"지ꡬ μ˜¨λ‚œν™” ν˜„μƒμ„ λ―ΏμœΌμ‹­λ‹ˆκΉŒ?"
01:52
Well, consider these facts.
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이런 사싀듀을 ν•œλ²ˆ μƒκ°ν•΄λ³΄μ„Έμš”.
01:55
The American Association for the Advancement of Science, AAAS,
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미ꡭ과학진ν₯ν˜‘νšŒ(AAAS)λŠ”
01:58
one of the leading organizations in science,
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κ³Όν•™λΆ„μ•Όμ˜ 선두 주자 쀑 ν•˜λ‚˜μΈλ°μš”.
02:01
queried scientists and the public on different science topics.
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κ³Όν•™μžλ“€κ³Ό λŒ€μ€‘μ—κ²Œ λ‹€μ–‘ν•œ κ³Όν•™μ£Όμ œμ— λŒ€ν•œ μ§ˆλ¬Έμ„ ν–ˆμŠ΅λ‹ˆλ‹€.
02:05
Here are some of them:
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μ—¬κΈ° κ·Έ 쀑 λͺ‡λͺ‡ 질문이 μžˆμ–΄μš”.
02:06
genetically modified food, animal research, human evolution.
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μœ μ „μž λ³€ν˜• μ‹ν’ˆ, 동물연ꡬ, μΈκ°„μ˜ 진화 λ“± μž…λ‹ˆλ‹€.
02:11
And look at what the scientists say about those,
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그리고 κ³Όν•™μžλ“€μ΄ 이 μ£Όμ œλ“€μ— λŒ€ν•΄ μ–΄λ–»κ²Œ λ§ν•˜λŠ”μ§€ λ³΄μ„Έμš”.
02:14
the people that actually study those topics, in red,
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μ΄λŸ¬ν•œ μ£Όμ œλ“€μ— λŒ€ν•΄ μ‹€μ œλ‘œ μ—°κ΅¬ν•˜λŠ” 뢄듀이 빨간색 κ·Έλž˜ν”„μž…λ‹ˆλ‹€.
02:16
versus the gray, what the public thinks.
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νšŒμƒ‰ κ·Έλž˜ν”„λŠ” λŒ€μ€‘μ΄ μ–΄λ–»κ²Œ μƒκ°ν•˜λŠ”μ§€λ₯Ό λ³΄μ—¬μ€λ‹ˆλ‹€
02:19
How did we get there?
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μ–΄λ–»κ²Œ 이런 의견 차이가 λ‚˜λŠ” κ²ƒμΌκΉŒμš”?
02:21
How did we get there?
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μ–΄λ–»κ²Œ 이런 의견 차이가 λ‚˜λŠ” κ²ƒμΌκΉŒμš”?
02:24
That scientists and the public are so far apart on these science issues.
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κ³Όν•™μžλ“€κ³Ό λŒ€μ€‘μ΄ 과학적 μ΄μŠˆλ“€μ— λŒ€ν•΄ λ„ˆλ¬΄λ‚˜ λ‹€λ₯΄κ²Œ μƒκ°ν•˜κ³  μžˆμ–΄μš”.
02:29
Well, I'll come a little bit closer to home for me,
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이제 제 뢄야와 쑰금 더 μ—°κ΄€λœ μ–˜κΈ°λ₯Ό ν•˜μžλ©΄,
02:31
climate change.
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기후변화에 λŒ€ν•΄μ„œμš”.
02:33
Eighty-seven percent of scientists
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87%의 κ³Όν•™μžλ“€μ€
02:36
believe that humans are contributing to climate change.
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인간이 기후변화에 영ν–₯을 끼치고 μžˆλ‹€κ³  λ―ΏμŠ΅λ‹ˆλ‹€.
02:41
But only 50 percent of the public?
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ν•˜μ§€λ§Œ 겨우 50%의 λŒ€μ€‘λ“€λ§Œμ΄ κ·Έλ ‡κ²Œ μƒκ°ν•œλ‹€λ‹ˆμš”?
02:45
How did we get there?
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μ™œ 이런 ν˜„μƒμ΄ μΌμ–΄λ‚¬μ„κΉŒμš”?
02:46
So it begs the question,
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이 ν˜„μƒμ€ μ΄λŸ¬ν•œ μ˜λ¬Έμ„ λ“€κ²Œ ν•˜λŠ”λ°μš”.
02:48
what shapes perceptions about science?
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과학에 λŒ€ν•œ 인식을 ν˜•μ„±ν•˜λŠ” 것은 λ¬΄μ—‡μΌκΉŒμš”?
02:54
It's an interesting question
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이것은 ν₯미둜운 μ§ˆλ¬Έμž…λ‹ˆλ‹€.
02:56
and one that I've been thinking about quite a bit.
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그리고 μ œκ°€ μ˜€λž«λ™μ•ˆ μƒκ°ν•΄λ³΄μ•˜λ˜ λ¬Έμ œμ΄κΈ°λ„ ν•©λ‹ˆλ‹€.
03:00
I think that one thing that shapes perceptions in the public, about science,
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제 μƒκ°μ—λŠ” 과학에 λŒ€ν•œ λŒ€μ€‘λ“€μ˜ 인식을 ν˜•μ„±ν•˜λŠ” 것 쀑 ν•˜λ‚˜κ°€
03:05
is belief systems and biases.
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신념체계와 편견인 것 κ°™μŠ΅λ‹ˆλ‹€.
03:08
Belief systems and biases.
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신념체계와 νŽΈκ²¬μ΄μš”.
03:09
Go with me for a moment.
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잠깐 저와 ν•¨κ»˜ μƒκ°ν•΄λ³΄μ‹œμ£ .
03:12
Because I want to talk about three elements of that:
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신념체계와 편견의 μ„Έ 가지 μš”μ†Œμ— λŒ€ν•΄ μ–˜κΈ°ν•΄λ³΄κ³  μ‹ΆμŠ΅λ‹ˆλ‹€.
03:14
confirmation bias, Dunning-Kruger effect
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ν™•μ¦νŽΈν–₯κ³Ό 더닝-크루거 효과
03:18
and cognitive dissonance.
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그리고 인지 뢀쑰화에 λŒ€ν•΄μ„œμš”.
03:20
Now, these sound like big, fancy, academic terms, and they are.
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이 μš©μ–΄λ“€μ΄ 크고 멋지고 학문적인 κ²ƒμ²˜λŸΌ λ“€λ¦¬λŠ”λ° μ‹€μ œλ‘œ κ·Έλ ‡μŠ΅λ‹ˆλ‹€.
03:24
But when I describe them, you're going to be like, "Oh!
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ν•˜μ§€λ§Œ μ œκ°€ 이 μš©μ–΄λ“€μ„ μ„€λͺ…ν•˜λ©΄, μ—¬λŸ¬λΆ„λ“€μ€ μ•„λ§ˆλ„
03:28
I recognize that; I even know somebody that does that."
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"μ•„! 뭔지 μ•Œκ² λ„€μš”, 심지어 제 지인 쀑에 μ €λŸ¬λŠ” μ‚¬λžŒμ΄ μžˆμ–΄μš”." 라고 말할 κ²ƒμž…λ‹ˆλ‹€.
03:33
Confirmation bias.
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ν™•μ¦νŽΈν–₯은
03:36
Finding evidence that supports what we already believe.
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μš°λ¦¬κ°€ 이미 λ―Ώκ³  μžˆλŠ” 것을 λ’·λ°›μΉ¨ν•˜λŠ” 증거λ₯Ό μ°ΎλŠ” κ²ƒμž…λ‹ˆλ‹€.
03:40
Now, we're probably all a little bit guilty of that at times.
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μ•„λ§ˆλ„ 우리 λͺ¨λ‘κ°€ 가끔 μ΄λ ‡κ²Œ ν•˜κΈ° λ•Œλ¬Έμ— 찔릴 것 κ°™μ€λ°μš”.
03:45
Take a look at this.
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λ“€μ–΄λ³΄μ„Έμš”.
03:46
I'm on Twitter.
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μ €λŠ” νŠΈμœ„ν„°λ₯Ό ν•©λ‹ˆλ‹€.
03:48
And often, when it snows,
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그리고 가끔 눈이 였면,
03:50
I'll get this tweet back to me.
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μ €λŠ” 이런 νŠΈμœ—μ„ λ°›μ•„μš”.
03:52
(Laughter)
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(μ›ƒμŒ)
03:55
"Hey, Dr. Shepherd, I have 20 inches of global warming in my yard,
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"μ…°νΌλ“œ λ°•μ‚¬λ‹˜, 제 정원에 20인치짜리 μ§€κ΅¬μ˜¨λ‚œν™”κ°€ μžˆλŠ”λ°μš”.
03:58
what are you guys talking about, climate change?"
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κΈ°ν›„ λ³€ν™”λΌλ‹ˆ, 무슨 말을 ν•˜λŠ” κ±°μ˜ˆμš”?"
04:01
I get that tweet a lot, actually.
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μ €λŠ” 사싀 이런 νŠΈμœ—μ„ 많이 λ°›μŠ΅λ‹ˆλ‹€.
04:04
It's a cute tweet, it makes me chuckle as well.
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μ €λ₯Ό μ›ƒκ²Œ λ§Œλ“œλŠ” κ·€μ—¬μš΄ νŠΈμœ—μ΄μ£ .
04:07
But it's oh, so fundamentally scientifically flawed.
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ν•˜μ§€λ§Œ 이런 νŠΈμœ—μ€ 근본적으둜 과학적 결점이 μžˆμŠ΅λ‹ˆλ‹€.
04:12
Because it illustrates
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μ™œλƒν•˜λ©΄ 그건
04:13
that the person tweeting doesn't understand
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νŠΈμœ—ν•˜λŠ” μ‚¬λžŒμ΄
04:15
the difference between weather and climate.
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날씨와 κΈ°ν›„μ˜ 차이점을 μ΄ν•΄ν•˜μ§€ λͺ»ν•¨μ„ 보여주기 λ•Œλ¬Έμž…λ‹ˆλ‹€.
04:19
I often say, weather is your mood
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μ €λŠ” μ’…μ’… λ‚ μ”¨λŠ” λ‹Ήμ‹ μ˜ 기뢄이고,
04:23
and climate is your personality.
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κΈ°ν›„λŠ” λ‹Ήμ‹ μ˜ 성격이라고 λ§ν•©λ‹ˆλ‹€.
04:26
Think about that.
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ν•œλ²ˆ μƒκ°ν•΄λ³΄μ„Έμš”.
04:28
Weather is your mood, climate is your personality.
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λ‚ μ”¨λŠ” λ‹Ήμ‹ μ˜ 기뢄이고, κΈ°ν›„λŠ” λ‹Ήμ‹ μ˜ μ„±κ²©μ΄λΌλŠ” κ²ƒμ„μš”.
04:30
Your mood today doesn't necessarily tell me anything about your personality,
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였늘 λ‹Ήμ‹ μ˜ 기뢄이 λ‹Ήμ‹ μ˜ 성격을 λ³΄μ—¬μ£Όμ§€λŠ” μ•Šμ•„μš”.
04:34
nor does a cold day tell me anything about climate change,
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μΆ”μš΄ 날이 κΈ°ν›„λ³€ν™”λ₯Ό λ‚˜νƒ€λ‚΄λŠ” 것이 μ•„λ‹Œ 것 μ²˜λŸΌμš”,
04:37
or a hot day, for that matter.
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λ˜λŠ” λ”μš΄ 날이 κΈ°ν›„λ³€ν™”λ₯Ό λ³΄μ—¬μ£Όμ§€λŠ” μ•Šμ£ .
04:41
Dunning-Kruger.
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더닝-크루거 νš¨κ³Όμž…λ‹ˆλ‹€.
04:43
Two scholars from Cornell came up with the Dunning-Kruger effect.
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코넬 μΆœμ‹ μΈ 두 λͺ…μ˜ ν•™μžκ°€ 더닝-크루거 효과λ₯Ό μƒκ°ν•΄λƒˆμŠ΅λ‹ˆλ‹€.
04:46
If you go look up the peer-reviewed paper for this,
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μƒν˜Έ 심사 논문을 검색해 보신닀면
04:49
you will see all kinds of fancy terminology:
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μ—¬λŸ¬κ°€μ§€ ν™”λ €ν•œ μš©μ–΄λ“€μ„ 보싀 κ±°μ˜ˆμš”.
04:51
it's an illusory superiority complex, thinking we know things.
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싀체가 μ—†λŠ” μš°μ›” μ½€ν”Œλ ‰μŠ€μ£ .
04:55
In other words, people think they know more than they do.
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즉, μ‚¬λžŒλ“€μ€ μžμ‹ μ΄ μ‹€μ œλ‘œ μ•„λŠ” 것보닀 더 많이 μ•Œκ³  μžˆλ‹€κ³  μƒκ°ν•΄μš”.
04:59
Or they underestimate what they don't know.
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ν˜Ήμ€ μžμ‹ μ΄ μ–Όλ§ˆλ‚˜ λͺ¨λ₯΄λŠ”지λ₯Ό κ³Όμ†Œν‰κ°€ν•©λ‹ˆλ‹€.
05:02
And then, there's cognitive dissonance.
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그리고 인지 λΆ€μ‘°ν™”κ°€ μžˆμŠ΅λ‹ˆλ‹€.
05:06
Cognitive dissonance is interesting.
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μΈμ§€λΆ€μ‘°ν™”λŠ” ν₯λ―Έλ‘­μŠ΅λ‹ˆλ‹€.
05:09
We just recently had Groundhog Day, right?
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μ΅œκ·Όμ— μ„±μ΄‰μ ˆμ΄μ—ˆμ£ ?
05:13
Now, there's no better definition of cognitive dissonance
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인지뢀쑰화에 λŒ€ν•œ μ •μ˜λŠ”
05:15
than intelligent people asking me if a rodent's forecast is accurate.
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ν˜„λͺ…ν•œ μ‚¬λžŒλ“€μ΄ μ €μ—κ²Œ μ„€μΉ˜λ₯˜κ°€ μ˜ˆλ³΄ν•œ 게 μ •ν™•ν•œμ§€λ₯Ό μ‹€μ œλ‘œ λ¬Όμ–΄λ³Έλ‹€λŠ” κ²ƒμž…λ‹ˆλ‹€.
05:19
(Laughter)
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(μ›ƒμŒ)
05:22
But I get that, all of the time.
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ν•˜μ§€λ§Œ μ €λŠ” 항상 이런 μ§ˆλ¬Έμ„ λ°›μ•„μš”.
05:24
(Laughter)
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(μ›ƒμŒ)
05:26
But I also hear about the Farmer's Almanac.
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μ €λŠ” 농사 연감에 λŒ€ν•΄μ„œλ„ μ§ˆλ¬Έμ„ λ°›μŠ΅λ‹ˆλ‹€.
05:29
We grew up on the Farmer's Almanac, people are familiar with it.
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μš°λ¦¬λŠ” 농사 연감에 μ˜ν•΄ μ»Έμ–΄μš”, μ‚¬λžŒλ“€μ€ 그것에 μ΅μˆ™ν•΄μš”.
05:34
The problem is, it's only about 37 percent accurate,
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λ¬Έμ œλŠ” 농사 μ—°κ°μ˜ 정확도가 37% 밖에 λ˜μ§€ μ•ŠλŠ”λ‹€λŠ” κ²ƒμž…λ‹ˆλ‹€.
05:37
according to studies at Penn State University.
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νŽœμ‹€λ² λ‹ˆμ•„ 주립 λŒ€ν•™κ΅μ— λ”°λ₯΄λ©΄ 말이죠.
05:43
But we're in an era of science
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ν•˜μ§€λ§Œ μš°λ¦¬λŠ”
05:47
where we actually can forecast the weather.
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μ‹€μ œλ‘œ 날씨λ₯Ό μ˜ˆμΈ‘ν•  수 μžˆλŠ” κ³Όν•™μ˜ μ‹œλŒ€μ— μ‚΄κ³  μžˆμŠ΅λ‹ˆλ‹€.
05:49
And believe it or not, and I know some of you are like, "Yeah, right,"
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λ―Ώκ±°λ‚˜ λ§κ±°λ‚˜, μ €λŠ” μ—¬λŸ¬λΆ„ 쀑 λͺ‡λͺ‡μ€ "λ§žμ•„, 그래." 라고 ν•˜λŠ” 것을 μ•Œμ•„μš”.
05:52
we're about 90 percent accurate, or more, with weather forecast.
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날씨 μ˜ˆλ³΄λŠ” 90% 정도 μ •ν™•ν•©λ‹ˆλ‹€.
05:55
You just tend to remember the occasional miss, you do.
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μ–΄μ©Œλ‹€ ν‹€λ¦¬λŠ” κ²ƒλ“€λ§Œ κΈ°μ–΅ν•˜μ…”μ„œ κ·Έλž˜μš”.
05:58
(Laughter)
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(μ›ƒμŒ)
06:02
So confirmation bias, Dunning-Kruger and cognitive dissonance.
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ν™•μ¦νŽΈν–₯, 더닝-크루거 νš¨κ³Όμ™€ 인지 λΆ€μ‘°ν™”
06:05
I think those shape biases and perceptions that people have about science.
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이것듀이 과학에 λŒ€ν•œ μ‚¬λžŒλ“€μ˜ 편견과 인식을 λ§Œλ“€μ–΄ λ‚΄λŠ” 것 κ°™μ•„μš”.
06:11
But then, there's literacy and misinformation
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그리고 κ±°κΈ°μ—λŠ” 이해와 μ˜€ν•΄κ°€ μžˆμŠ΅λ‹ˆλ‹€.
06:13
that keep us boxed in, as well.
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그것이 우리λ₯Ό ν•œκ³„μ— 가두어 두고 있죠.
06:17
During the hurricane season of 2017,
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2017λ…„ νƒœν’ μ‹œκΈ°μ—,
06:20
media outlets had to actually assign reporters
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언둠사듀은 κΈ°μžλ“€μ„ μ§€μ •ν•΄μ„œ
06:24
to dismiss fake information about the weather forecast.
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λ‚ μ”¨μ˜ˆλ³΄μ— λŒ€ν•΄ κ°€μ§œ 정보λ₯Ό 내보내라고 ν–ˆμ–΄μš”.
06:30
That's the era that we're in.
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μš°λ¦¬κ°€ μ‚΄κ³  μžˆλŠ” μ‹œλŒ€μ˜ μ΄μ•ΌκΈ°μž…λ‹ˆλ‹€.
06:32
I deal with this all the time in social media.
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μ €λŠ” μ†Œμ…œλ―Έλ””μ–΄μ—μ„œ 늘 κ²ͺλŠ” μΌμ΄μ—μš”.
06:35
Someone will tweet a forecast --
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λˆ„κ΅°κ°€ 날씨 예보λ₯Ό νŠΈμœ„ν„°μ— 올리고
06:36
that's a forecast for Hurricane Irma, but here's the problem:
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ν—ˆλ¦¬μΌ€μΈ μ΄λ§ˆμ— λŒ€ν•œ μ˜ˆλ³΄μΈλ°μš”, 거기에 λ¬Έμ œκ°€ μžˆμ–΄μš”.
06:39
it didn't come from the Hurricane Center.
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κ·Έ μ •λ³΄λŠ” ν—ˆλ¦¬μΌ€μΈ μ„Όν„°μ—μ„œ λ‚˜μ˜¨ 게 μ•„λ‹ˆμ—ˆμ–΄μš”.
06:42
But people were tweeting and sharing this; it went viral.
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ν•˜μ§€λ§Œ μ‚¬λžŒλ“€μ€ 이것을 κ³΅μœ ν–ˆκ³  μ—„μ²­λ‚˜κ²Œ νΌμ‘Œμ–΄μš”.
06:45
It didn't come from the National Hurricane Center at all.
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ν•˜μ§€λ§Œ ꡭ립 ν—ˆλ¦¬μΌ€μΈ μ„Όν„°μ—μ„œ 온 정보가 μ•„λ‹ˆμ—ˆμ–΄μš”.
06:50
So I spent 12 years of my career at NASA
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μ§€λ‚œ 12λ…„λ™μ•ˆ NASAμ—μ„œ μΌν•˜κ³ 
06:52
before coming to the University of Georgia,
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μ‘°μ§€μ•„λŒ€ν•™μ— 였기 전에,
06:54
and I chair their Earth Science Advisory Committee,
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지ꡬ과학 μžλ¬Έμœ„μ›νšŒλ₯Ό μ£Όμž¬ν•˜λ©΄μ„œ,
06:57
I was just up there last week in DC.
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μ§€λ‚œμ£Όμ— μ›Œμ‹±ν„΄ D.C에 μžˆμ—ˆμ–΄μš”.
06:59
And I saw some really interesting things.
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그리고 κ±°κΈ°μ„œ 맀우 ν₯미둜운 점듀을 λ°œκ²¬ν–ˆμŠ΅λ‹ˆλ‹€.
07:01
Here's a NASA model and science data from satellite
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NASA λͺ¨λΈκ³Ό μœ„μ„± κ³Όν•™ μžλ£ŒμΈλ°μš”.
07:04
showing the 2017 hurricane season.
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2017λ…„ ν—ˆλ¦¬μΌ€μΈ μ‹œκΈ°λ₯Ό λ³΄μ—¬μ€λ‹ˆλ‹€.
07:06
You see Hurricane Harvey there?
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저기에 ν—ˆλ¦¬μΌ€μΈ ν•˜λΉ„ λ³΄μ΄μ‹œλ‚˜μš”?
07:09
Look at all the dust coming off of Africa.
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μ•„ν”„λ¦¬μΉ΄μ—μ„œλΆ€ν„° μ˜€λŠ” 먼지듀을 λ³΄μ„Έμš”.
07:12
Look at the wildfires up in northwest US and in western Canada.
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미ꡭ의 λΆμ„œλΆ€μ™€ μΊλ‚˜λ‹€ μ„œλΆ€ 지역에 μžˆλŠ” μ‚°λΆˆμ„ λ³΄μ„Έμš”.
07:17
There comes Hurricane Irma.
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κ±°κΈ°μ„œ ν—ˆλ¦¬μΌ€μΈ μ–΄λ§ˆκ°€ μ™”μ–΄μš”.
07:20
This is fascinating to me.
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μ €μ—κ²ŒλŠ” μ΄λŸ¬ν•œ 것이 맀우 ν₯λ―Έλ‘­μŠ΅λ‹ˆλ‹€.
07:23
But admittedly, I'm a weather geek.
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λ¬Όλ‘ , μ €λŠ” 날씨 κ΄΄μ§œμž…λ‹ˆλ‹€.
07:26
But more importantly, it illustrates that we have the technology
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이것은 μš°λ¦¬κ°€
07:30
to not only observe the weather and climate system,
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날씨와 기후체계λ₯Ό κ΄€μ°°λ§Œ ν•  수 μžˆλŠ” 것이 μ•„λ‹ˆλΌ,
07:33
but predict it.
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μ˜ˆμΈ‘ν•  수 μžˆλ‹€λŠ” 것을 λ³΄μ—¬μ€λ‹ˆλ‹€.
07:34
There's scientific understanding,
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μ—¬κΈ°μ—λŠ” 과학적 기반이 있기 λ•Œλ¬Έμ—
07:36
so there's no need for some of those perceptions and biases
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방금 μ „κΉŒμ§€ μ–˜κΈ°ν–ˆλ˜ 인식과 νŽΈκ²¬λ“€μ΄
07:39
that we've been talking about.
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ν•„μš”ν•˜μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.
07:41
We have knowledge.
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μš°λ¦¬μ—κ²ŒλŠ” 지식이 μžˆμ–΄μš”.
07:42
But think about this ...
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ν•˜μ§€λ§Œ 생각해 λ³΄μ„Έμš”.
07:43
This is Houston, Texas, after Hurricane Harvey.
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이것은 ν—ˆλ¦¬μΌ€μΈ ν•˜λΉ„κ°€ μ§€λ‚˜κ°„ ν›„ νœ΄μŠ€ν„΄, ν…μ‚¬μŠ€μ˜ λͺ¨μŠ΅μž…λ‹ˆλ‹€.
07:47
Now, I write a contribution for "Forbes" magazine periodically,
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μ €λŠ” 포브슀 μž‘μ§€μ— μ •κΈ°μ μœΌλ‘œ κΈ°κ³ λ₯Ό ν•˜λŠ”λ°μš”.
07:50
and I wrote an article a week before Hurricane Harvey made landfall, saying,
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ν—ˆλ¦¬μΌ€μΈ ν•˜λΉ„κ°€ 였기 일주일 전에 기사λ₯Ό μΌμ–΄μš”.
07:55
"There's probably going to be 40 to 50 inches of rainfall."
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"μ•„λ§ˆ 40μ—μ„œ 50인치 μ •λ„μ˜ λΉ„κ°€ 내릴 것 κ°™μŠ΅λ‹ˆλ‹€." λΌκ³ μš”
07:58
I wrote that a week before it happened.
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μ‹€μ œλ‘œ νƒœν’ 상λ₯™ 1주일 전에 μ“΄ κΈ€μž…λ‹ˆλ‹€.
08:01
But yet, when you talk to people in Houston,
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그런데 μ •μž‘ νœ΄μŠ€ν„΄μ— μ‚΄κ³ μžˆλŠ” μ£Όλ―Όλ“€κ³Ό μ–˜κΈ°λ₯Ό ν•˜λ©΄
08:03
people are saying, "We had no idea it was going to be this bad."
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μ‚¬λžŒλ“€μ€ μ΄λ ‡κ²Œ λ§ν•©λ‹ˆλ‹€, "정말 μ΄λ ‡κ²Œ 심각할 쀄은 μ „ν˜€ λͺ°λžμ–΄."
08:07
I'm just...
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μ €λŠ” κ·Έλƒ₯...
08:08
(Sigh)
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(ν•œμˆ¨)
08:09
(Laughter)
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(μ›ƒμŒ)
08:10
A week before.
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일주일 μ „μ΄μ—μš”.
08:11
But --
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ν•˜μ§€λ§Œ
08:13
I know, it's amusing, but the reality is,
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μ•Œμ•„μš”, μž¬λ―Έμžˆμ–΄μš”. ν•˜μ§€λ§Œ ν˜„μ‹€μ€
08:15
we all struggle with perceiving something outside of our experience level.
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μš°λ¦¬κ°€ κ²½ν—˜ν•΄λ³΄μ§€ λͺ»ν•œ 것을 λ°›μ•„λ“€μ΄λŠ” 게 μ–΄λ ΅λ‹€λŠ” κ²ƒμž…λ‹ˆλ‹€.
08:21
People in Houston get rain all of the time,
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νœ΄μŠ€ν„΄μ—λŠ” 늘 λΉ„κ°€ λ‚΄λ¦½λ‹ˆλ‹€.
08:24
they flood all of the time.
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항상 ν™μˆ˜κ°€ μΌμ–΄λ‚˜μš”.
08:26
But they've never experienced that.
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ν•˜μ§€λ§Œ κ·Έ μ •λ„λ‘œ μ‹¬κ°ν•œ ν™μˆ˜λŠ” κ²½ν—˜ν•΄λ³΄μ§€ λͺ»ν•œ 것이죠.
08:29
Houston gets about 34 inches of rainfall for the entire year.
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νœ΄μŠ€ν„΄μ˜ 1λ…„ κ°•μš°λŸ‰μ€ 34μΈμΉ˜μž…λ‹ˆλ‹€.
08:33
They got 50 inches in three days.
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그런데 3μΌλ§Œμ— 50인치의 λΉ„κ°€ λ‚΄λ Έμ–΄μš”.
그것은 기상 μ΄λ³€μž…λ‹ˆλ‹€. μ •μƒμ˜ λ²”μœ„λ₯Ό λ²—μ–΄λ‚œ μΌμ΄μ—ˆμ–΄μš”.
08:37
That's an anomaly event, that's outside of the normal.
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08:42
So belief systems and biases, literacy and misinformation.
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신념체계와 편견, 이해와 μ˜€ν•΄
08:45
How do we step out of the boxes that are cornering our perceptions?
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이λ₯Ό κ·Ήλ³΅ν•˜κΈ° μœ„ν•΄μ„  μ–΄λ–»κ²Œ ν•΄μ•Όν• κΉŒμš”?
08:50
Well we don't even have to go to Houston, we can come very close to home.
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νœ΄μŠ€ν„΄μ— λ°˜λ“œμ‹œ 가지 μ•Šμ•„λ„ 집 κ·Όμ²˜μ—μ„œλ„ κ°€λŠ₯ν•œ 일이죠,
08:54
(Laughter)
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(μ›ƒμŒ)
08:55
Remember "Snowpocalypse?"
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μŠ€λ…Έν¬μΉΌλ¦½μŠ€λΌλŠ” μ‹ μ‘°μ–΄λ₯Ό κΈ°μ–΅ν•˜μ‹œλ‚˜μš”?
08:57
(Laughter)
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(μ›ƒμŒ)
08:59
Snowmageddon?
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μŠ€λ…Έλ§€κ°€λˆμ€μš”?
09:00
Snowzilla?
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μŠ€λ…Έμ§ˆλΌλŠ”μš”?
09:02
Whatever you want to call it.
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뭐라고 λΆ€λ₯΄λ“ 
09:04
All two inches of it.
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2인치 밖에 λ˜μ§€ μ•Šμ•˜μ£ .
09:06
(Laughter)
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(μ›ƒμŒ)
09:09
Two inches of snow shut the city of Atlanta down.
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2인치의 λˆˆμ— μ•„ν‹€λžœνƒ€ μ‹œλ‚΄κ°€ νμ‡„λ˜μ–΄ λ²„λ ΈμŠ΅λ‹ˆλ‹€.
09:11
(Laughter)
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(μ›ƒμŒ)
09:14
But the reality is, we were in a winter storm watch,
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ν•˜μ§€λ§Œ μ‹€μ œλ‘œ, κ²¨μšΈν­ν’μ— λŒ€ν•œ 경보λ₯Ό λ°›μ•˜κ³ 
09:19
we went to a winter weather advisory,
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겨울 기상 주의보λ₯Ό λ°›μ•˜μ–΄μš”.
09:21
and a lot of people perceived that as being a downgrade,
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그리고 λ§Žμ€ μ‚¬λžŒλ“€μ΄ 그것이 별일 아닐 것이라고 μƒκ°ν–ˆμ–΄μš”.
09:24
"Oh, it's not going to be as bad."
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"κ·Έλ ‡κ²Œ λ‚˜μ˜μ§€ μ•Šμ„ κ±°μ•Ό."
09:26
When in fact, the perception was that it was not going to be as bad,
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사싀은 별일 아닐 것이라고 μΈμ‹ν–ˆμ§€λ§Œ,
09:29
but it was actually an upgrade.
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μ‹€μ œλ‘œλŠ” 그렇지 μ•Šκ³  μ•…ν™”λ˜μ—ˆμ–΄μš”.
09:31
Things were getting worse as the models were coming in.
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기상관츑λͺ¨λΈλ“€μ΄ λ„μž…λ˜λ©΄μ„œ 상황이 더 λ‚˜λΉ μ‘Œμ–΄μš”.
09:33
So that's an example of how we get boxed in by our perceptions.
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μ΄λŸ¬ν•œ 것은 μ–Όλ§ˆλ‚˜ 우리 μžμ‹ μ˜ 인식 속에 κ°‡ν˜€μžˆλŠ”μ§€λ₯Ό λ³΄μ—¬μ€λ‹ˆλ‹€.
09:38
So, the question becomes,
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κ·Έλ ‡λ‹€λ©΄ μ§ˆλ¬Έμ€,
09:40
how do we expand our radius?
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μƒκ°μ˜ λ°˜κ²½μ„ μ–΄λ–»κ²Œ λ„“ν˜€μ•Ό ν• κΉŒμš”?
09:45
The area of a circle is "pi r squared".
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μ›μ˜ λ„“μ΄λŠ” 파이 μ•Œ μ œκ³±μž…λ‹ˆλ‹€.
09:47
We increase the radius, we increase the area.
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λ°˜μ§€λ¦„μ„ λ„“νžˆλ©΄ μ›μ˜ 넓이도 컀지겠죠.
09:50
How do we expand our radius of understanding about science?
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과학을 μ΄ν•΄ν•˜λŠ” μƒκ°μ˜ λ°˜κ²½μ„ μ–΄λ–»κ²Œ λ„“ν˜€μ•Ό ν• κΉŒμš”?
09:54
Here are my thoughts.
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제 생각은 μ΄λ ‡μŠ΅λ‹ˆλ‹€.
09:56
You take inventory of your own biases.
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λ¨Όμ € 슀슀둜의 νŽΈκ²¬μ— λŒ€ν•΄μ„œ 성찰을 ν•΄λ³΄μ„Έμš”.
09:59
And I'm challenging you all to do that.
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μ—¬λŸ¬λΆ„ λͺ¨λ‘ ν•΄λ³΄μ‹œλ„λ‘ κΆŒν•©λ‹ˆλ‹€.
10:01
Take an inventory of your own biases.
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슀슀둜의 νŽΈκ²¬μ— λŒ€ν•΄μ„œ 성찰을 ν•΄λ³΄μ„Έμš”.
10:04
Where do they come from?
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그것듀이 μ–΄λ””μ„œ μ˜€λ‚˜μš”?
10:06
Your upbringing, your political perspective, your faith --
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μ—¬λŸ¬λΆ„μ˜ κ°€μ • ꡐ윑, μ •μΉ˜μ  μ‹œκ°, 믿음
10:09
what shapes your own biases?
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μ—¬λŸ¬λΆ„μ˜ νŽΈκ²¬μ„ λ§Œλ“œλŠ” 것이 λ¬΄μ—‡μž…λ‹ˆκΉŒ?
10:13
Then, evaluate your sources --
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κ·Έ λ‹€μŒ κ·Έκ²ƒμ˜ 좜처λ₯Ό κ°€λŠ ν•΄λ΄…λ‹ˆλ‹€.
10:15
where do you get your information on science?
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과학에 λŒ€ν•œ μ •λ³΄λŠ” μ–΄λ””μ„œ λ“€μœΌμ‹œλ‚˜μš”?
10:18
What do you read, what do you listen to,
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무엇을 μ½μœΌμ‹œκ³ , 무엇을 λ“€μœΌμ‹œλ‚˜μš”?
10:20
to consume your information on science?
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과학에 λŒ€ν•œ 정보λ₯Ό μ–»κΈ° μœ„ν•΄μ„œμš”?
10:23
And then, it's important to speak out.
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그리고 이에 λŒ€ν•΄ μ†”μ§ν•˜κ²Œ λ§ν•˜λŠ” 것이 μ€‘μš”ν•©λ‹ˆλ‹€.
10:25
Talk about how you evaluated your biases and evaluated your sources.
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μ—¬λŸ¬λΆ„μ˜ 편견과 μΆœμ²˜λ“€μ„ μ–΄λ–»κ²Œ ν‰κ°€ν–ˆλŠ”μ§€ 말이죠.
10:29
I want you to listen to this little 40-second clip
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40초짜리 짧은 μ˜μƒμ„ μž μ‹œ λ³΄μ…¨μœΌλ©΄ ν•©λ‹ˆλ‹€.
10:32
from one of the top TV meteorologists in the US, Greg Fishel,
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λ―Έκ΅­ μƒμœ„ TV κΈ°μƒν•™μž κ·Έλ ‰ ν”Όμ‰˜,
10:37
in the Raleigh, Durham area.
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둀리, λ”λŸΌ 지역에 μ‚΄κ³  μžˆμŠ΅λ‹ˆλ‹€.
10:39
He's revered in that region.
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κ·Έ μ§€μ—­μ—μ„œ μ‘΄κ²½λ°›λŠ” μ‚¬λžŒμ΄μ—μš”.
10:40
But he was a climate skeptic.
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ν•˜μ§€λ§Œ κ·ΈλŠ” 기후에 λŒ€ν•΄ μ˜μ‹¬μ΄ λ§Žμ•˜μ–΄μš”.
10:42
But listen to what he says about speaking out.
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ν•˜μ§€λ§Œ κ·Έκ°€ 곡개적으둜 λ§ν•˜λŠ” λ‚΄μš©μ„ λ“€μ–΄λ³΄μ„Έμš”.
10:44
Greg Fishel: The mistake I was making
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κ·Έλ ‰ ν”Όμ…€: μ œκ°€ 저지λ₯Έ μ‹€μˆ˜λŠ”
10:46
and didn't realize until very recently,
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μ΅œκ·Όμ—μ•Ό κΉ¨λ‹¬μ•˜λŠ”λ°μš”.
10:48
was that I was only looking for information
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μ œκ°€ 보고자 ν–ˆλ˜ μ •λ³΄λŠ”
10:50
to support what I already thought,
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기쑴에 μ œκ°€ μƒκ°ν–ˆλ˜κ²ƒκ³Ό λΆ€ν•©ν•˜λŠ” 것 λΏμ΄μ—ˆμŠ΅λ‹ˆλ‹€.
10:53
and was not interested in listening to anything contrary.
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그리고 그와 λ°˜λŒ€μΈ κ²ƒμ—λŠ” μ „ν˜€ 관심이 μ—†μ—ˆμ–΄μš”.
10:58
And so I woke up one morning,
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μ–΄λŠλ‚  μ•„μΉ¨ μΌμ–΄λ‚˜μž,
11:00
and there was this question in my mind,
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ν•œ 질문이 제 머릿속에 λ– μ˜¬λžμŠ΅λ‹ˆλ‹€.
11:04
"Greg, are you engaging in confirmation bias?
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"κ·Έλ ‰, ν™•μ¦νŽΈν–₯을 ν•˜κ³  μžˆλŠ” κ±°λ‹ˆ?
11:07
Are you only looking for information to support what you already think?"
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λ„€κ°€ μƒκ°ν•˜κ³  μžˆλŠ” 것듀을 뒷받침해쀄 수 μžˆλŠ” μ •λ³΄λ§Œ μ°ΎλŠ” κ±°μ•Ό?"
11:12
And if I was honest with myself, and I tried to be,
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제 μžμ‹ μ—κ²Œ μ†”μ§ν•΄μ§€μžλ©΄,
11:14
I admitted that was going on.
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κ·Έλ ‡λ‹€κ³  μΈμ •ν–ˆμŠ΅λ‹ˆλ‹€.
11:17
And so the more I talked to scientists
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κ·Έλž˜μ„œ κ³Όν•™μžλ“€κ³Ό 더 λ§Žμ€ 이야기λ₯Ό λ‚˜λˆ„κ³ ,
11:19
and read peer-reviewed literature
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μƒν˜Έ 심사 논문을 읽고,
11:21
and tried to conduct myself the way I'd been taught to conduct myself
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νŽœμ‹€λ² λ‹ˆμ•„ 주립 λŒ€ν•™κ΅ ν•™μƒμ΄μ—ˆμ„ λ•Œ
11:26
at Penn State when I was a student,
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λ°°μ› λ˜λ°λ‘œ ν–‰λ™ν•˜λ €κ³  λ…Έλ ₯ν–ˆμ§€λ§Œ,
11:29
it became very difficult for me to make the argument
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μ•„λ¬΄λŸ° 영ν–₯이 μ—†λ‹€κ³  μ£Όμž₯ν•˜κΈ°κ°€
11:32
that we weren't at least having some effect.
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맀우 μ–΄λ €μ› μŠ΅λ‹ˆλ‹€.
11:34
Maybe there was still a doubt as to how much,
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ν˜Ήμ—¬λ‚˜ μ˜ν˜Ήμ„ κ°€μ‘Œμ„ μˆ˜λ„ μžˆκ² μ§€λ§Œ,
11:36
but to say "nothing" was not a responsible thing for me to do
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"아무것도 μ—†λ‹€"라고 λ§ν•˜κΈ°μ—”, μ±…μž„κ°μžˆλŠ” 일이 μ•„λ‹ˆμ—ˆμ–΄μš”.
11:41
as a scientist or a person.
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κ³Όν•™μžλ‘œμ„œλ„ ν•œ μΈκ°„μœΌλ‘œμ„œλ„ 말이죠.
11:45
JMS: Greg Fishel just talked about expanding his radius
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κ·Έλ ‰ ν”Όμ‰˜μ€ μžμ‹ μ˜ λ°˜κ²½μ„ λ„“νžˆλŠ” 것에 λŒ€ν•΄ μ–˜κΈ°ν–ˆμŠ΅λ‹ˆλ‹€.
11:49
of understanding of science.
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과학을 μ΄ν•΄ν•˜λŠ”λ° μžˆμ–΄μ„œ 말이죠.
11:50
And when we expand our radius,
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그리고 μš°λ¦¬κ°€ λ°˜κ²½μ„ λ„“νžˆλŠ” 것은,
11:52
it's not about making a better future,
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더 λ‚˜μ€ 미래λ₯Ό λ§Œλ“€μ–΄ λ‚˜κ°€λŠ” 것이 μ•„λ‹ˆλΌ,
11:56
but it's about preserving life as we know it.
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μš°λ¦¬κ°€ μ•Œκ³  μžˆλŠ” 삢을 μ œλŒ€λ‘œ μœ μ§€ν•˜λŠ” 것에 λŒ€ν•œ μΌμž…λ‹ˆλ‹€.
12:00
So as we think about expanding our own radius in understanding science,
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κ·Έλž˜μ„œ 과학을 이해함에 μžˆμ–΄μ„œ 우리 슀슀둜의 λ°˜κ²½μ„ λ„“νžˆλŠ” 것은
12:06
it's critical for Athens, Georgia, for Atlanta, Georgia,
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μ•„ν…Œλ„€, 쑰지아와 μ• ν‹€λžœνƒ€, 쑰지아
12:09
for the state of Georgia, and for the world.
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그리고 쑰지아주와 μ „ 세계λ₯Ό μœ„ν•΄ λŒ€λ‹¨νžˆ μ€‘μš”ν•©λ‹ˆλ‹€.
12:12
So expand your radius.
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κ·ΈλŸ¬λ‹ˆ λ‹Ήμ‹ μ˜ λ°˜κ²½μ„ λ„“νžˆμ„Έμš”.
12:14
Thank you.
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κ°μ‚¬ν•©λ‹ˆλ‹€.
12:16
(Applause)
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(λ°•μˆ˜)
이 μ›Ήμ‚¬μ΄νŠΈ 정보

이 μ‚¬μ΄νŠΈλŠ” μ˜μ–΄ ν•™μŠ΅μ— μœ μš©ν•œ YouTube λ™μ˜μƒμ„ μ†Œκ°œν•©λ‹ˆλ‹€. μ „ 세계 졜고의 μ„ μƒλ‹˜λ“€μ΄ κ°€λ₯΄μΉ˜λŠ” μ˜μ–΄ μˆ˜μ—…μ„ 보게 될 κ²ƒμž…λ‹ˆλ‹€. 각 λ™μ˜μƒ νŽ˜μ΄μ§€μ— ν‘œμ‹œλ˜λŠ” μ˜μ–΄ μžλ§‰μ„ 더블 ν΄λ¦­ν•˜λ©΄ κ·Έκ³³μ—μ„œ λ™μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€. λΉ„λ””μ˜€ μž¬μƒμ— 맞좰 μžλ§‰μ΄ μŠ€ν¬λ‘€λ©λ‹ˆλ‹€. μ˜κ²¬μ΄λ‚˜ μš”μ²­μ΄ μžˆλŠ” 경우 이 문의 양식을 μ‚¬μš©ν•˜μ—¬ λ¬Έμ˜ν•˜μ‹­μ‹œμ˜€.

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