Section C: Reading Comprehension
Directions: There are 2 passages in this section. Each passage is followed by some questions or unfinished statements. For each of them there are four choices marked A), B), C) and D). You should decide on the best choice and mark the corresponding letter on Answer Sheet 2 with a single line through the centre.
Passage One
Questions 46 to 50 are based on the following passage.
Technology is never a neutral tool for achieving human ends. Technological innovations reshape people as they use these innovations to control their environment. Artificial intelligence, for example, is altering humanity.
While the term AI conjures up anxieties about killer robots or catastrophic levels of unemployment, there are other, deeper implications. As AI increasingly shapes the human experience, how does this change what it means to be human? Central to the problem is a person’s capacity to make choices, particularly judgments that have moral implications.
Aristotle argued that the capacity for making practical judgments depends on regularly making them—on habit and practice. We see the emergence of machines as substitute judges in a variety of everyday contexts as a potential threat to people learning how to effectively exercise judgment themselves.
In the workplace, managers routinely make decisions about who to hire or fire and which loan to approve, to name a few. These are areas where algorithmic prescription is replacing human judgment, and so people who might have had the chance to develop practical judgment in these areas no longer will.
Recommendation engines, which are increasingly prevalent intermediaries in people’s consumption of culture, may serve to constrain choice and minimize luck. By presenting consumers with algorithmically selected choices of what to watch, read, stream and visit next, companies are replacing human taste with machine taste. In one sense, this is helpful. After all, machines can survey a wider range of choices than any individual is likely to have the time or energy to do on their own.
At the same time, though, this selection is optimizing for what people are likely to prefer based on what they’ve preferred in the past. We think there is some risk that people’s options will be constrained by their past in a new and unanticipated way.
As machine learning algorithms improve and as they train on more extensive data sets, larger parts of everyday life are likely to become utterly predictable. The predictions are going to get better and better, and they will ultimately make common experiences more efficient and pleasant.
Algorithms could soon—if they don’t already—have a better idea about which show you’d like to watch next and which job candidate you should hire than you do. One day, humans may even find a way for machines to make these decisions without some of the biases that humans typically display.
But to the extent that unpredictability is part of how people understand themselves and part of what people like about themselves, humanity is in the process of losing something significant. As they become more and more predictable, the creatures inhabiting the increasingly AI-mediated world will become less and less like us.
46. What do we learn about the deeper implications of AI?
47. What is the consequence of algorithmic prescription replacing human judgment?
48. What may result from increasing application of recommendation engines in our consumption of culture?
49. What is likely to happen to larger parts of our daily life as machine learning algorithms improve?
50. Why does the author say the creatures living in the more and more AI-mediated world will become increasingly unlike us?
Passage Two
Questions 51 to 55 are based on the following passage.
Phonics, which involves sounding out words syllable by syllable, is the best way to teach children to read. But in many classrooms, this can be a dirty word. So much so that some teachers have had to sneak phonics teaching materials into the classroom. Most American children are taught to read in a way that study after study has found to be wrong.
The consequences of this are striking. Less than half of all American adults were proficient readers in 2017. American fourth graders rank 15th on the Progress in International Literacy Study, an international exam.
America is stuck in a debate about teaching children to read that has been going on for decades. Some advocate teaching symbol-sound relationships (the sound k can be spelled as c, k, ck, or ch), known as phonics. Others support an immersive approach (using pictures of a cat to learn the word cat), known as “whole language”. Most teachers today, almost three out of four according to a survey by the EdWeek Research Centre in 2019, use a mix called “balanced literacy”. This combination of methods is ineffective. “You can’t sprinkle in a little phonics,” says Tenette Smith, executive director of elementary education and reading at Mississippi’s education department. “It has to be systematic and explicitly taught.”
Mississippi, often behind in social policy, has set an example here. In a state once notorious for its low reading scores, the Mississippi state legislature passed new literacy standards in 2013. Since then Mississippi has seen remarkable gains. Its fourth graders have moved from 49th (out of 50 states) to 29th on the National Assessment of Educational Progress, a nationwide exam. In 2019 it was the only state to improve its scores. For the first time since measurement began, Mississippi’s pupils are now average readers, a remarkable achievement in such a poor state.
Mississippi’s success is attributed to implementing reading methods supported by a body of research known as the science of reading. In 1997 Congress requested the National Institute of Child Health and Human Development and the Department of Education to convene a National Reading Panel to end the “reading wars” and synthesize the evidence. The panel found that phonics, along with explicit instruction in phonemic awareness, fluency and comprehension, worked best.
Yet over two decades on, “balanced literacy” is still being taught in classrooms. But advances in statistics and brain imaging have disproved the whole-language method. To the teacher who is a proficient reader, literacy seems like a natural process that requires educated guessing, rather than the deliberate process emphasized by phonics. Teachers can imagine that they learned to read through osmosis when they were children. Without proper training, they bring this to classrooms.
51. What do we learn about phonics in many American classrooms?
52. What has America been witnessing for decades?
53. Why does Tenette Smith think a combination of teaching methods is ineffective?
54. What does the author say Mississippi s success is attributed to?
55. What have advances in statistics and brain imaging proved ineffective?
Answers & Explanations (答案与解析)
Passage One
46. D。解析：题干问关于AI深层含义我们了解到什么。第一段指出“Artificial intelligence, for example, is altering humanity.”（人工智能正在改变人类）。第二段接着探讨“As AI increasingly shapes the human experience, how does this change what it means to be human?”（这如何改变成为人类的意义）。altering humanity 即 reshaping humanity。
47. A。解析：题干问算法处方取代人类判断的后果。第四段明确指出，由于算法取代了人类的判断，“people who might have had the chance to develop practical judgment in these areas no longer will.”（那些本来有机会在这些领域培养实际判断力的人将不再有这个机会）。对应选项A（人们失去了培养做出实际判断能力的机会）。
48. A。解析：题干问在文化消费中越来越多地应用推荐引擎可能导致什么结果。第五段指出推荐引擎“may serve to constrain choice”（可能起到限制选择的作用）。第六段重申“people’s options will be constrained by their past”（人们的选项将被他们的过去所限制）。对应选项A（消费者的选择将大受限制）。
49. C。解析：题干问随着机器学习算法的进步，我们日常生活的很大一部分可能会发生什么。第七段指出“larger parts of everyday life are likely to become utterly predictable”（日常生活的很大一部分可能会变得完全可预测）。utterly predictable 与 completely anticipated 是同义替换。
50. B。解析：题干问为什么作者说生活在越来越多由AI介导的世界中的生物将变得越来越不像我们。最后一段指出，不可预测性（unpredictability）是人们理解自己和喜欢自己的一部分。随着人们变得越来越可预测，他们将变得越来越不像我们。对应选项B（他们将不再拥有不可预测的人类特征）。
全文翻译
技术从来不是实现人类目的的中性工具。技术创新在使用过程中重塑着使用它们来控制环境的人。例如，人工智能正在改变人类。
虽然「人工智能」这个词会让人联想到杀手机器人或灾难性的失业率，但还有其他更深层次的影响。随着AI日益塑造人类体验，这如何改变人之为人的意义？问题的核心在于一个人做出选择的能力，特别是具有道德含义的判断。
亚里士多德认为，做出实际判断的能力取决于经常做出这些判断——取决于习惯和实践。我们把机器在各种日常场景中作为替代判断者的出现，视为对人们学习如何自己有效行使判断力的潜在威胁。
在职场中，管理者经常做出招谁、解雇谁、批准哪笔贷款等决策。在这些领域，算法规定正在取代人类判断，因此那些本来可能有机会在这些领域培养实际判断力的人将不再有这样的机会。
推荐引擎——在人们文化消费中日益普遍的媒介——可能会限制选择并减少偶然性。通过向消费者呈现算法选择的接下来看什么、读什么、流媒体播放什么、访问什么的选项，公司正在用机器品味替代人类品味。从某种意义上说，这是有帮助的。毕竟，机器能够扫描比任何个人可能拥有的时间或精力去独自处理更广泛的选择范围。
然而，与此同时，这种选择正在基于人们过去喜欢什么来优化他们可能喜欢什么。我们认为存在某种风险，即人们的选择将以一种新的、未曾预料的方式被他们的过去所限制。
随着机器学习算法的改进，以及它们在更广泛的数据集上训练，日常生活的更大一部分可能变得完全可预测。预测会变得越来越好，它们最终将使共同体验更加高效和愉快。
算法可能很快就会——如果还没有的话——比你更清楚你接下来想看哪个节目以及你应该雇用哪个求职者。有一天，人类甚至可能找到一种让机器做出这些决定的方法，同时消除人类通常表现出的某些偏见。
但是，在不可预测性是人们理解自我的一部分，也是人们喜欢自己的部分中，人类正在失去某种重要的东西。随着他们变得越来越可预测，那些生活在日益由AI介导的世界中的生物将变得越来越不像我们。
Passage Two
51. A。解析：题干问在许多美国教室里我们对自然拼读法了解多少。第一段指出，在许多教室里，自然拼读法“can be a dirty word”（可能是一个脏字/禁忌词），导致老师不得不偷偷把材料带进教室。dirty word 比喻名声不好，对应选项A（它名声不好，ill reputed）。
52. B。解析：题干问几十年来美国一直在见证什么。第三段明确指出“America is stuck in a debate about teaching children to read that has been going on for decades.”（美国陷入了一场关于如何教孩子阅读的争论，这场辩论已经持续了几十年）。对应选项B（关于教孩子阅读的方法的持久辩论）。
53. B。解析：题干问为什么 Tenette Smith 认为结合教学法是无效的。第三段中她表示：“You can’t sprinkle in a little phonics... It has to be systematic and explicitly taught.”（你不能只撒一点点自然拼读法...它必须被系统地、明确地教授）。对应选项B（自然拼读法必须系统地应用和清晰地教授才能达到预期效果）。
54. C。解析：题干问作者说密西西比州的成功归功于什么。第五段指出“Mississippi’s success is attributed to implementing reading methods supported by a body of research known as the science of reading.”（密西西比州的成功归功于实施了由被称为阅读科学的大量研究支持的阅读方法）。对应选项C（采用了有科学依据的方法来教授阅读）。
55. D。解析：题干问统计学和大脑成像的进步证明什么无效。最后一段指出“advances in statistics and brain imaging have disproved the whole-language method.”（统计学和大脑成像的进步已经反驳/证明了整体语言法无效）。结合第三段可知，whole-language method 即 immersive approach（沉浸式方法）。对应选项D。
全文翻译
自然拼读法，即逐音节发出单词读音，是教孩子阅读的最佳方法。但在许多教室里，这可能是一个忌讳的词。以至于一些教师不得不偷偷将自然拼读教材带进教室。大多数美国儿童以一种损害他们阅读能力的方式被教授阅读。他们被教使用提示词（诸如图片和上下文）来猜测单词——这种技巧熟练的读者会使用，但初学读者不会。问题在于，虽然事实证明在阅读教学中使用自然拼读法和直接教学法对所有儿童都是有益的，但对那些在语音意识方面存在困难的人尤其至关重要。这点早已被实证研究证实。不过，新的做法并非基于最新的研究，而是基于这样的信念：学会阅读就像学会说话一样是自然的，不需要直接教授基本技能。事实上，这两种过程截然不同。言语是天生的；而阅读必须被有意识地学习。这听起来可能有点像老生常谈了。但问题之所以持续存在，是因为所谓"阅读之战"还在继续，且没有尽头。倡导直接阅读教学法（如自然拼读法）的教育者面临来自其他教师和学校教育者的巨大阻力。许多人将其称之为"操练加扼杀"，并说孩子们不需要这些基本技能。直接教学法的倡导者则说，不使用自然拼读法会导致孩子们因未能成为成功的阅读者而遭受不必要的痛苦。没有一种教授阅读的方法是完美的。但是，如果让我们在这两种教学方法之间做选择，那么那些正在遭受不必要痛苦的孩子才应该是我们首要考虑的对象。而他们目前正在遭受痛苦。2013年，密西西比州四年级阅读障碍学生比例最高的地区是密西西比州。密西西比州开始大力培训教师，在课堂上使用基于自然拼读法的教学方式。到2019年，密西西比州已成为全国唯一一个在阅读方面取得显著成绩提高的州。他们从第四差的州变成了全国最好的州之一。来自富有的受过高等教育白人家庭的学生比来自低社会经济群体的学生更容易成为成功的阅读者，这已不是秘密。但是，我们有必要问一下，是不是那些有能力支付私人阅读辅导费用的富裕家庭的孩子才在轻松享受阅读？与此同时，不成比例的有色人种儿童却因被剥夺了最佳的阅读教学方法而继续落后。
核心搭配与高分句型
【核心搭配与高频短语】
conjure up：使在脑海中浮现，想起（conjures up anxieties about killer robots）
to name a few：举几个例子（which loan to approve, to name a few）
in one sense：在某种意义上（In one sense, this is helpful.）
stuck in：陷入，卡在（stuck in a debate about teaching children to read）
attributed to：归因于（Mississippi’s success is attributed to implementing reading methods）
【亮点句型解析】
To the extent that 引导的程度状语从句：
"But to the extent that unpredictability is part of how people understand themselves and part of what people like about themselves, humanity is in the process of losing something significant."
（但是在某种程度上，不可预测性是人们如何理解自己以及人们喜欢自己的一部分，那么人类在这个过程中正在失去一些重要的东西。）
"But to the extent that unpredictability is part of how people understand themselves and part of what people like about themselves, humanity is in the process of losing something significant."
（但是在某种程度上，不可预测性是人们如何理解自己以及人们喜欢自己的一部分，那么人类在这个过程中正在失去一些重要的东西。）
to the extent that 表示“在...程度上”，使得论述既严谨又留有余地。 So much so that 引导的程度结果状语：
"But in many classrooms, this can be a dirty word. So much so that some teachers have had to sneak phonics teaching materials into the classroom."
（但在许多教室里，这可能是一个禁忌词。以至于一些老师不得不偷偷地将自然拼读教学材料带进教室。）
"But in many classrooms, this can be a dirty word. So much so that some teachers have had to sneak phonics teaching materials into the classroom."
（但在许多教室里，这可能是一个禁忌词。以至于一些老师不得不偷偷地将自然拼读教学材料带进教室。）
so much so that 意为“到如此程度以至于”，生动地刻画了自然拼读法在某些学校受到排斥的严重程度。