2023학년도 수능특강 유형편 제목 파악 본문 지문 6강 보면서 mp3

2023학년도 수능특강 유형편 제목 파악 본문 지문 6강 보면서 mp3

06 제목 파악_22005-0292_통파일.mp3

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1.    Although cognitive and neuropsychological approaches emphasize the losses with age that might impair social perception, motivational theories indicate that there may be some gains or qualitative changes.

인식적 접근법과 신경 심리학적 접근법이 사회 지각을 해칠지도 모르는 노화에 따른 상실을 강조하긴 하지만, 동기 이론은 어떤 이점이나 질적 변화가 있을 수 있다는 것을 보여 준다.

 

2.     Charles and Carstensen review a considerable body of evidence indicating that, as people get older, they tend to prioritize close social relationships, focus more on achieving emotional well-being, and attend

more to positive emotional information while ignoring negative information.

Charles와 Carstensen은 사람들은 나이가 들면서 친밀한 사회적 관계를 우선시하고, 정서적 행복을 성취하는 데 더 주력하 고, 부정적인 정보는 무시하는 반면에 긍정적인 정보에 더 많이 주의를 기울이는 경향이 있다는 것을 보여 주는 상당한 양의 증거 를 검토한다.

 

3.     These changing motivational goals in old age have implications for attention to and processing of social cues from the environment.

노년의 이런 변화하는 동기 부여상의 목표는 주변 환경으로부터의 사회적 신호를 주목하고 처리하는 것에 영향을 미친다.

 

4.      Of particular importance in considering emotional changes in old age is the presence of a positivity bias: that is, a tendency to notice, attend to, and remember more positive compared to negative information.

노년의 정서적 변화를 고려할 때 특히 중요한 것은 긍정 편향, 즉 부정적 정보에 비해 더 긍정적인 정보를 인지하고, 주목하고, 기 억하는 경향이 있다는 것이다.

 

5.     The role of life experience in social skills also indicates that older adults might show gains in some aspects of social perception.

사회적 기술(사교적 능력)에서 인생 경험이 하는 역할 또한 노년의 성인이 사회 지각의 일부 측면에서 이점을 보여 줄 수 있다는 것을 나타낸다.

*cognitive 인식의 **impair 해치다

 

 

 

1.    Up until the late 1970s, the United States produced at least 70 percent of the clothing that Americans purchased.

1970년대 후반까지, 미국은 자국민이 구매하는 의류의 최소 70퍼센트를 생산했다.

 

2.     And — thanks to the New Deal — for much of the twentieth century, brands and manufacturers were expected to follow strict national labor laws.

그리고 뉴딜 정책 덕분에 20세기의 대부분 동안, (의류) 브랜드와 제조사는 엄격한 국가 노동법들을 따라야 했다.

 

3.     But in the late 1980s, a new section of the clothing business cropped up: "fast fashion," the production of trendy, inexpensive clothes in vast amounts at lightning speed in subcontracted factories, to be sold in

thousands of chain stores.

그러나 1980년대 후반에 의류 사업의 새로운 부분이 불쑥 나타났는데, 이는 수천 개의 연쇄점에서 판매할 수 있도록, 하청 계약 된 공장에서 아주 빠른 속도로 대량으로 최신 유행의 비싸지 않은 의류를 생산하는 ‘패스트 패션’이었다.

 

4.      To keep the prices low, fast-fashion brands cut manufacturing costs — and the cheapest labor was available in the world's poorest countries.

가격을 낮게 유지하기 위해, 패스트 패션 브랜드는 제조 비용을 삭감했는데, 가장 비용이 들지 않는 노동자는 세계에서 제일 가난 한 나라에서 얻을 수 있었다.

 

5.     Offshoring caught on across the industry, just as globalization was unfurling.

막 세계화가 차례로 펼쳐지면서, 생산 기지 해외 이전이 의류 산업 전반에 걸쳐 인기를 얻었다.

 

6.     Though it started as a small corner of the business, fast fashion's remarkable success was so enviable it

 

soon reset the rhythm for how clothing —

advertised, and sold.

from luxury to athletic wear —

was and is conceived,

 

패스트 패션은 그 업계의 작은 외딴 부분으로 시작했지만, 그 놀라운 성공은 참으로 부러운 것이어서 이는 곧 고급 의류에서 운동 복에 이르기까지 의류가 구상되고, 광고되며, 판매된 방식, 그리고 그렇게 되고 있는 방식에 대한 리듬을 재설정했다.

 

7.     The impact was dramatic: in the last thirty years, fashion has grown from a $500 billion trade, primarily domestically produced, to a $2.4-trillion-a-year global giant.

그 영향력은 대단히 컸는데, 지난 30년 동안에 패션 산업은 주로 국내에서 생산된 5천억 달러의 거래에서 연간 2조 4천억 달러 의 국제적인 거대 산업으로 성장했다.

*unfurl (상황 등이) 차례로 펼쳐지다

 

 

 

1.    The fact that deep learning computers merely carry out programmatic functions without understanding what they are doing or any implications has created problems in the past.

딥 러닝 컴퓨터가 자신이 무엇을 하고 있는지, 혹은 그 어떤 결과도 이해하지 못하고 프로그램에 따른 기능을 수행할 뿐이라는 사 실은 지금까지 문제를 일으켜 왔다.

 

2.     In particular, many analysts have described a problem of algorithmic bias.

특히, 많은 분석가들은 알고리즘 편향의 문제를 설명해 왔다.

 

3.     The data sets that neural networks train on are representations of the world as it is rather than the world that we might like to see.

신경 회로망이 토대로 삼아 훈련하는 데이터 세트는 우리가 보고 싶어 할지도 모르는 세계라기보다는 있는 그대로의 세계에 대 한 표현이다.

 

4.      Because of this, a deep learning algorithm may reproduce the worst stereotypes and biases of our society as a whole.

이 때문에 딥 러닝 알고리즘은 우리 사회 전체의 최악의 고정 관념과 편향을 재현할 수 있다.

 

5.     A human, on the other hand, might exercise judgment and question the patterns that he or she was seeing.

반면에 인간은 판단력을 발휘하여 자신이 보고 있었던 패턴에 의문을 제기할지도 모른다.

 

6.     For example, a deep learning network might see that most of the pictures labeled as “secretary” in an online archive are female, whereas most pictures of a “boss” are male, and conclude that men are

always bosses and women are always secretaries.

예를 들어, 딥 러닝 회로망은 온라인 수집 자료에서 ‘비서’라고 분류된 대부분의 사진은 여성이지만, ‘상사’의 대부분의 사진은 남 성이라는 것을 알아채고, 남성은 항상 상사이고 여성은 항상 비서라는 결론을 내릴지도 모른다.

*algorithmic 알고리즘의 **archive 수집 자료, (데이터 등의) 집적

 

 

 

1.    Assumptions are a reflection of what's going on in one’s mind, where we focus our thinking.

가정(假定)이란 우리 마음속에서 무슨 일이 일어나고 있는지, 즉 우리 사고의 초점을 어디에 두고 있는지를 반영한다.

 

2.     When our thoughts are voiced to someone else, we now have a great opportunity to learn the person’s perception.

우리 생각이 다른 어떤 사람에게 말로 표현되면 이제 우리는 그 사람의 인식을 알게 될 좋은 기회를 얻은 것이다.

 

3.     It is precisely from that perception that change efforts can start.

변화의 노력이 시작될 수 있는 것은 바로 그 인식으로부터이다.

 

4.      Suppose Alex was thinking, “I'm too old to get that job. They're looking for someone fresh out of school.” As a result, Alex did not apply for the teaching position, even though he really wanted it.

Alex가 “나는 나이가 너무 많아서 저 일자리를 얻을 수 없어. 학교를 갓 졸업한 사람을 찾고 있네”라고 생각하고 있었다고 가정 해 보자. 그 결과 Alex는 그 가르치는 자리를 정말 원했으면서도 거기에 지원하지 않았다.

 

5.     Suppose he was a great leader who could turn that school around, but his lack of initiative created a lose-lose for him and for the school district.

그가 그 학교 전체를 호전시킬 수 있는 훌륭한 지도자였지만 그의 진취성 부족이 그와 그 학구 모두에 부정적 결과를 낳는 상황을 만들었다고 가정해 보자.

 

6.     What if instead he recognized he had unique experiences from his prior roles that could indeed be of true value? If he had been willing to challenge his own assumption, he might have taken action, with

confidence, and pursued his goal. Stories like this happen too often.

그러는 대신, 그가 정말로 진정한 가치가 있을 수도 있는 이전의 역할에서 온 특별한 경험을 가지고 있다는 것을 깨달았다면 어땠 을까? 만약 자신의 가정의 진실을 기꺼이 의심하려고 했다면, 그는 자신 있게 행동을 취하여 자신의 목표를 추구했을지도 모른 다. 이와 같은 이야기들은 너무나 자주 일어난다.

 

7.     They hinder progress and inhibit organizations from achieving their goals.

그것들은 앞으로 나아가는 것을 가로막고 조직이 목표를 달성하는 것을 저해한다.

 

8.     They cause individuals to remain locked in their self-imposed boundaries.

그것들은 사람들이 스스로 부과한 한계 안에 묶여 있도록 한다.

 

 

 

1.    Physicians claim that a lot of what they do is intuitive.

의사들은 자신들이 하는 일 중 많은 부분이 직관적이라고 주장한다.

 

2.     It is reasoning through the associations built up over years of practice.

그것은 여러 해에 걸친 업무에서 축적된 연관성을 통한 추론이다.

 

3.     But when AI scientists work on the problem of medical diagnosis, their effort is to see the diagnostic process as a set of explicit procedures that can be captured in a program.

그러나 인공 지능 과학자들이 의학 진단 문제를 연구할 때, 그들이 하는 일은 진단 과정을 프로그램에 담길 수 있는 일련의 명시적 절차로 보는 것이다.

 

4.      AI experts attack the problem by interviewing a physician over the course of months, trying to pin down every aspect of how he or she makes decisions.

인공 지능 전문가들은 여러 달에 걸쳐 의사를 인터뷰함으로써 그 문제에 착수하여, 그 사람이 결정하는 방식의 모든 면을 정확히 밝히려고 노력한다.

 

5.     They model the structure of that practical knowledge which “feels intuitive.”

그들은 ‘직관적으로 느껴지는’ 그 실제적인 지식의 구조 모형을 만든다.

 

6.     The resulting program will, given the same information as the physician, usually come to the same conclusion.

그 결과로 나온 프로그램은 의사와 동일한 정보를 제공받을 경우 보통 똑같은 결론에 도달할 것이다.

 

7.     The process of writing such programs has a side effect.

그런 프로그램을 작성하는 과정에는 뜻하지 않은 결과가 생긴다.

 

8.     If the program “thinks” henceforth like the physician, the physician’s thinking about his or her activity has been changed by collaboration in the making of the program.

이후로 그 프로그램이 그 의사처럼 ‘생각한다’고 해도, 자신의 활동에 대한 의사의 생각은 그 프로그램을 만들 때의 협업에 의해 바뀐 상태이다.

 

9.     What once seemed intuitive to the physician has been shown to be formalizable.

전에는 의사에게 직관적이라고 보였던 것이 공식화가 가능한 것으로 드러났다.

 

 

Although cognitive and neuropsychological approaches emphasize the losses with age that might impair social perception, motivational theories indicate that there may be some gains or qualitative changes. Charles and Carstensen review a considerable body of evidence indicating that, as people get older, they tend to prioritize close social relationships, focus more on achieving emotional well-being, and attend more to positive emotional information while ignoring negative information. These changing motivational goals in old age have implications for attention to and processing of social cues from the environment. Of particular importance in considering emotional changes in old age is the presence of a positivity bias: that is, a tendency to notice, attend to, and remember more positive compared to negative information. The role of life experience in social skills also indicates that older adults might show gains in some aspects of social perception.

6-1

Up until the late 1970s, the United States produced at least 70 percent of the clothing that Americans purchased. And - thanks to the New Deal - for much of the twentieth century, brands and manufacturers were expected to follow strict national labor laws. But in the late 1980s, a new section of the clothing business cropped up: "fast fashion," the production of trendy, inexpensive clothes in vast amounts at lightning speed in subcontracted factories, to be sold in thousands of chain stores. To keep the prices low, fast-fashion brands cut manufacturing costs - and the cheapest labor was available in the world's poorest countries. Offshoring caught on across the industry, just as globalization was unfurling. Though it started as a small corner of the business, fast fashion's remarkable success was so enviable it soon reset the rhythm for how clothing - from luxury to athletic wear - was and is conceived, advertised, and sold. The impact was dramatic: in the last thirty years, fashion has grown from a $500 billion trade, primarily domestically produced, to a $2.4-trillion-a-year global giant.

6-2

The fact that deep learning computers merely carry out programmatic functions without understanding what they are doing or any implications has created problems in the past. In particular, many analysts have described a problem of algorithmic bias. The data sets that neural networks train on are representations of the world as it is rather than the world that we might like to see. Because of this, a deep learning algorithm may reproduce the worst stereotypes and biases of our society as a whole. A human, on the other hand, might exercise judgment and question the patterns that he or she was seeing. For example, a deep learning network might see that most of the pictures labeled as "secretary" in an online archive are female, whereas most pictures of a "boss" are male, and conclude that men are always bosses and women are always secretaries.

6-3

Assumptions are a reflection of what's going on in one's mind, where we focus our thinking. When our thoughts are voiced to someone else, we now have a great opportunity to learn the person's perception. It is precisely from that perception that change efforts can start. Suppose Alex was thinking, "I'm too old to get that job. They're looking for someone fresh out of school." As a result, Alex did not apply for the teaching position, even though he really wanted it. Suppose he was a great leader who could turn that school around, but his lack of initiative created a lose-lose for him and for the school district. What if instead he recognized he had unique experiences from his prior roles that could indeed be of true value? If he had been willing to challenge his own assumption, he might have taken action, with confidence, and pursued his goal. Stories like this happen too often. They hinder progress and inhibit organizations from achieving their goals. They cause individuals to remain locked in their self-imposed boundaries.

6-4

Physicians claim that a lot of what they do is intuitive. It is reasoning through the associations built up over years of practice. But when AI scientists work on the problem of medical diagnosis, their effort is to see the diagnostic process as a set of explicit procedures that can be captured in a program. AI experts attack the problem by interviewing a physician over the course of months, trying to pin down every aspect of how he or she makes decisions. They model the structure of that practical knowledge which "feels intuitive." The resulting program will, given the same information as the physician, usually come to the same conclusion. The process of writing such programs has a side effect. If the program "thinks" henceforth like the physician, the physician's thinking about his or her activity has been changed by collaboration in the making of the program. What once seemed intuitive to the physician has been shown to be formalizable.