高考英语二轮-阅读理解议论文攻略（复习讲义）（北京专用）（原卷版)

这是一份高考英语二轮-阅读理解议论文攻略（复习讲义）（北京专用）（原卷版)，共54页。试卷主要包含了议论文高频考点，议论文中低频考点等内容，欢迎下载使用。
TOC \ "1-3" \h \u
\l "_Tc13774" 01 考情解码·命题预警 PAGEREF _Tc13774 \h 2
\l "_Tc3524" 02 体系构建·思维可视 PAGEREF _Tc3524 \h 3
\l "_Tc22792" 03 核心突破·靶向攻坚 PAGEREF _Tc22792 \h 3
\l "_Tc21307" 考点一 议论文高频考点 PAGEREF _Tc21307 \h 3
\l "_Tc22348" 知识点1 议论文概述 PAGEREF _Tc22348 \h 3
\l "_Tc27019" 知识点2 主旨大意题（最佳标题/作者观点） PAGEREF _Tc27019 \h 9
\l "_Tc13359" 知识点3 词义猜测题（划线词/短语含义） PAGEREF _Tc13359 \h 10
\l "_Tc31244" 知识点4 推理判断题（作者态度/意图/隐含意义） PAGEREF _Tc31244 \h 14
\l "_Tc31044" 考向1 考查主旨大意题 PAGEREF _Tc31044 \h 18
\l "_Tc670" 考向2 考查词义猜测题 PAGEREF _Tc670 \h 22
\l "_Tc17999" 考向3 考查推理判断题之深层推理 PAGEREF _Tc17999 \h 25
\l "_Tc14727" 考点二 议论文中低频考点 PAGEREF _Tc14727 \h 29
\l "_Tc18685" 知识点1 细节理解题（事实信息定位） PAGEREF _Tc18685 \h 29
\l "_Tc23020" 知识点2 推理判断题（观点态度/结构功能/文章出处/人物性格/目标读者/后续走势） PAGEREF _Tc23020 \h 29
\l "_Tc13796" 考向1 考查细节理解题 PAGEREF _Tc13796 \h 36
\l "_Tc16389" 考向2 考查推理判断题 PAGEREF _Tc16389 \h 38
\l "_Tc14155" 04真题溯源·考向感知 PAGEREF _Tc14155 \h 42
01 考情解码·命题预警
02 体系构建·思维可视
03 核心突破·靶向攻坚

考点一 议论文高频考点
\l "_Tc25045" 知识点1 议论文概述
一、观点论证类（高考最核心、最高频）
核心特征
作者明确提出一个中心论点（如 “科技使人疏远”“阅读纸质书更利于深度思考”），再通过分论点、论据（事实、数据、名言、案例等）层层论证观点的合理性，最终强化中心论点。
此类文章的关键是找到 “总 - 分 - 总” 或 “引论 - 本论 - 结论” 的结构，明确 “作者到底想证明什么”。
论证逻辑
引论：通过背景、现象、设问等引出中心论点（常位于首段结尾或第二段开头）；
本论：分论点 1 + 论据（如研究数据 / 名人观点）→ 分论点 2 + 论据 → （可选）反驳对立观点；
结论：重申中心论点，或提出建议、展望。
解题关键
定位中心论点（注意首段转折词 but/hwever、总结词 therefre，或第二段首句的新观点）；
2)区分 “论点”（作者的观点）和 “论据”（支撑观点的材料），避免将论据当主旨。
二、利弊分析类（高频，侧重辩证思考）
1.核心特征
针对某一现象、事物或行为（如 “社交媒体的使用”“线上教育”“人工智能的发展”），从 “利” 和 “弊” 两个维度展开分析，最终可能偏向某一立场，或保持中立、提出平衡建议。
此类文章的标志是有明显的 “正反对比” 逻辑，常出现表转折或对比的连接词。
论证逻辑
1)引入话题：介绍讨论对象（如 “近年来线上办公普及”）；
2)分析有利方面：分点阐述好处（如 “节省通勤时间”“灵活安排工作”），配具体例子；
3)分析不利方面：用转折词（hwever/n the ther hand）引出弊端（如 “缺乏面对面沟通”“工作与生活边界模糊”），配案例或数据；
4) 结论：① 偏向某一方（如 “利大于弊，应合理推广”）；② 中立建议（如 “需制定规则减少弊端”）。
解题关键
1)梳理 “利”“弊” 对应的具体内容，避免混淆；
2)关注结尾段的 “作者态度”（是否有明确倾向，还是建议平衡），这是主旨题的常考点。
三、问题解决类（侧重 “提出问题 - 分析原因 - 给出方案”）
1.核心特征
文章围绕一个具体问题（如 “环境污染”“青少年肥胖”“传统文化流失”）展开，先明确问题的表现或严重性，再分析问题产生的原因，最后提出解决问题的方法或建议。
此类文章的核心是 “问题→原因→对策” 的逻辑链，常出现表因果、建议的词汇。
论证逻辑
1)提出问题：描述问题的现象（如 “全球气温持续上升，极端天气增多”），或用数据说明问题的严重性；
2)分析原因：从多个角度（如社会、经济、个人、技术）分析问题根源（如 “温室气体排放”“森林砍伐”）；
3)解决对策：分点提出具体方案（如 “减少化石燃料使用”“推广可再生能源”“加强国际合作”）；
4)结尾：强调对策的重要性，或展望解决后的效果。
解题关键
1)理清 “问题 - 原因 - 对策” 的对应关系（如某对策是针对某一具体原因）；
2)注意 “建议类词汇”（suggest/prpse/recmmend/shuld/must），这类词汇后常是解题关键信息。
四、现象解释类（侧重 “是什么 - 为什么 - 怎么样”）
1.核心特征
针对某一特殊现象（如 “人们为何更愿意买品牌产品”“为什么熬夜现象普遍”“某些传统节日复兴”），先说明现象的具体表现，再深入分析现象背后的原因（如心理、社会、文化因素），最后可能补充现象的影响（积极 / 消极）。此类文章的重点是 “解释原因”，而非 “论证观点”，常涉及心理学、社会学等背景知识。
论证逻辑
1）引入现象：用具体案例、数据或生活场景描述现象（如 “调查显示，70% 的年轻人每周至少熬夜 3 次”）；
2）分析原因：从不同维度拆解（如心理原因：“白天压力大，夜晚想独处”；社会原因：“工作 / 学习任务重，不得不熬夜”；技术原因：“手机娱乐内容多，吸引熬夜”）；
3)补充影响：（可选）说明现象的后果（如 “熬夜导致免疫力下降”），或提出应对思路。
3. 解题关键
1)关注 “原因类信号词”（because/since/why/the reasn is that/due t）；
2)区分 “直接原因” 和 “根本原因”，避免只看表面现象。
五、对比论证类（侧重 “比较不同观点 / 事物”）
1.核心特征
将两个或多个相关的观点、事物、方法进行对比（如 “传统教育 vs 线上教育”“两种环保政策的效果”“不同文化对‘礼貌’的定义”），通过比较它们的异同、优缺点，最终可能得出 “哪一个更优” 或 “需结合使用” 的结论。
此类文章的结构多为 “总 - 分 - 总”，对比维度清晰（如效果、成本、适用人群）。
2.论证逻辑
1)引入对比对象：明确要比较的两个事物 / 观点（如 “关于‘是否应该禁止校园零食’，存在两种不同意见”）；
2) 分维度对比：从同一角度比较两者（如维度 1：健康影响 → 观点 A 认为…，观点 B 认为…；维度 2：学生需求 → 观点 A 认为…，观点 B 认为…）；
3) 结论：总结对比结果，提出作者倾向（如 “禁止零食虽利于健康，但需兼顾学生合理需求”）。
3.解题关键
1)梳理 “对比维度”（如健康、成本、效率），避免混淆不同维度的信息；
2)注意 “对比信号词”（while/whereas/in cntrast/n the cntrary），帮助定位异同点。
总结：议论文类型与解题关联
\l "_Tc16775" 知识点2 主旨大意题（最佳标题/作者观点）
一、【命题解读】
主旨大意题考查的是考生对文章内容的深层次理解,它要求考生在充分理解全文的前提下,对整篇文章的主旨大意有一个较为清晰的印象。主旨大意题不仅考查考生略读文章、领会大意的能力,也对考生的归纳、概括能力提出了较高的要求。文章中没有明显的解题依据,需要考生从文章中提炼、抽取一些关键词、主干句进行加工概括,才能归纳出文章的主旨。
【常考类型】
解题策略
①利用主题句在段首位置推敲段落大意
说明文和议论文学会关注“首段”和“段首”。借鉴“七选五”小标题类型特点，段首句统领全段主旨大意，由此推断段落大意。
②利用主题句在段中位置推敲段落大意
有时主题句出现在段中某句，这就需要考生耐心阅读揣摩段落各句之间内在逻辑关系，确定主题句位置，进而明确段落主旨大意。
③利用主题句在段尾位置推敲段落大意
有时主题句出现在段尾，关注一些表征总结性，结论性的词: in brief/ shrt, all in all, in cnclusin, in a wrd等，这些词后面连接的通常是主题句。
④利用段落没有主题句推敲段落大意
有时候段落没有出现主题句，需要考生自己总结提炼，难度更大。
⑤利用“总-分-总”结构推敲语篇主旨大意题
有时候段落没有出现主题句，需要考生自己总结提炼，难度更大。
\l "_Tc16775" 知识点3 词义猜测题（划线词/短语含义）
一、【猜测词义命题解读】
词句猜测题它可以是对一个单词的意义的推断,也可以是对一个短语或句子的意义的推断。词句猜测题既可以考查生词的意义,也可以考查熟词的新义,还可以是对替代词所替代内容的判断。在阅读理解题中,所考查的词或短语的意义往往不停留在字面上,而要根据短文提供的语境,通过阅读上下文,根据已知的信息或常识来推测尚不熟悉的词或短语的含义。
二、【猜测词义解题策略】
方法1：关注词的功能。授课过程中引导学生关注构词法、词的情感色彩、同义词、反义词、上下义词。
方法2：关注语篇中的解释功能。引导学生总结解释的几种方式：下定义、定语（从句）、同位语（从句）、举例子、标点符号等。
方法3：关注逻辑关系。引导学生总结几种常见的逻辑关系：因果、转折、并列、递进等。
三、词义(词组)猜测题设问方式
By saying that “...” in the first (secnd ...) paragraph, the authr means that ________.
In Paragraph ..., “...” can be replaced by “______”．
The meaning f “...” in Paragraph ... is related t ________.
Which f the fllwing has the clsest meaning t ... (Paragraph ...)？
As is used in Line ..., the wrd “...” refers t ________.
The underlined sentence in the ... paragraph prbably means that ________.
四、词义(词组)猜测题7大猜词技巧
要做好词义猜测题，考生除了必须熟练掌握《考试大纲》规定的词汇外，在平时的训练中还要注意积累生词和短语，掌握构词法的基本知识，对于各种前、后缀的变化形式了然于心，还要学会根据上下文语境进行合理推测，掌握一定的解题技巧。
1.利用释义法解题
在说明文尤其是科技类说明文中，作者通常会对一些关键词或专业术语进行解释。常见的有对该词下定义或后跟同位语、定语从句、冒号、破折号、括号等引出解释说明部分。通过阅读定义或解释部分，读者便可理解该词或短语的意思。
有时短文中出现一个需要猜测其意义的词或短语，下面接着出现其定义或解释。标点符号，如逗号后的解释(名词同位语)、破折号后的解释、括号内的解释等。这都是判断该词或短语意义的主要依据。例如：
①Annealing is a way f making metal sfter by heating it and then letting it cl very slwly.
句子给予了annealing一个明确的定义，即“退火”。
②It will be very hard but als very brittle — that is, it will break easily.
从that is(也就是说)后的解释中我们可以了解到，brittle是“脆的”意思。
③The herdsman,_wh lks after sheep, earns abut 650 yuan a year.
定语从句中lks after sheep就表明了herdsman的词义为“牧人”。
④The weather in this area is treacherus;_its sudden changes ften endanger the lives f sailrs.
分号后的句子在解释什么样的天气是treacherus, sudden change与treacherus在语义上相对应，因此含义是“突变的”。
⑤Sme gd readers find it helpful t use their sense t visualize — r picture — what they read.
visualize的意思由破折号后的picture(想象)给出了说明，因此含义为“想象”。
⑥When President Trrijs f Panama met Carter, he tried t give him a friendly abraz (hug)．
abraz对大多数人来说都很陌生，但由括号内的hug(拥抱)，我们不难推测abraz也是“拥抱”的意思。
2.利用同位关系进行猜测
阅读中出现的难词有时后面紧跟一个同位语，对前面的词进行解释，因此可利用同位关系对前面的词义或句意进行猜测。例如：
①They traveled a lng way, at last gt t a castle,_a large building in ld times.
同位语部分“a large building in ld times”给出了castle的确切词义，即古时候的“城堡”。
②We are n the night_shift — frm midnight t 8 a．m. — this week.
两个破折号之间的短语很清楚地表明night shift是“夜班”的意思。
③The “Chunnel”， a tunnel (隧道) cnnecting England and France, is nw cmplete.
此句中“a tunnel (隧道) cnnecting England and France”是Chunnel的同位语。因此，Chunnel指的就是英法之间的海底隧道。
3.利用构词法(前缀、后缀、派生等)进行猜测
在英语中，有很多词可以通过增加前缀和后缀的方式，构成新词。乍看起来，这个词可能是新词，但在掌握了一定的构词知识之后，就不难猜出它的词义。例如：
①“Our parties are aimed fr children 2 t 10，” Anacleri said, “and they're very interactive and creative in that they built a sense f drama based n a subject.”
文中interactive是由前缀inter(相互的)和active(活动的，活跃的)构成的，同时根据上下文的意思可以判断，该词的含义应是“互动的”。
②Perhaps, we can see sme pssibilities fr next fifty years. But the next hundred?
pssibility是pssible的同根名词，据此可以判断pssibility的意思是“可能性”。
4.利用因果关系进行猜测
在一篇阅读文章中，根据原因可以预测结果，根据结果也可以找出原因。例如：
①The lack f mvement caused the muscles t weaken. Smetimes the weakness was permanent. S the player culd never play the sprt again.
从后面的结果“永远不能再运动”可以推测permanent的意思为“永远的，永久的”。
②Mary didn't ntice me when I came int the classrm, because she was cmpletely engrssed in her reading.
从前面的结果“当我走进教室时，玛丽没有注意到我”可以推测engrssed的意思为“全神贯注的”。
③Our visin was bscured by the trees, s we culdn't see the lake frm ur windw.
由后面的结果culdn't see(看不见)可知，我们的视线被树遮挡住(bscured)了。
6.利用同义或近义关系进行猜测
在同一句、同一段或同一篇文章中，作者为了避免语言的单调、重复，有时会使用意思相同或相近的词。因此，考生只要读懂上下文，知道其中一个词的意思，就能猜出另外一个词的意思。
6.利用常识法解题
在仅靠分析篇章内在逻辑关系和语境无法猜出词义时，我们可以借助生活经验和普通常识确定词义。
7.利用转折或对比关系进行猜测
根据上下句的连接词，如but, hwever, therwise等可以推断上下文之间的逻辑关系，从而可以依据某一句的含义，来确定另一句的含义。另外，分号也可以表示转折、对比或不相干的意义。例如：
①A child's birthday party desn't have t be a hassle;_it can be a basket f fun.
从分号前后两句的意思可以看出，hassle和a basket f fun是相反的意义，所以不难判断hassle的意思是“困难，麻烦”。
②She is usually prmpt fr all her class, but tday she arrived in the middle f her first class.
but一词表示转折，因此but前后的意思正好相反。根据后半句的意思“她今天第一节课上了一半才来”，可得出她平时一向“准时”的结论。
③The players in the Wrld Cup are prfessinals, while thse wh play in the Olympics must be amateurs.
由于转折词“while”引导的两个分句前后意义相反，我们可推测出amateurs是prfessinals(专业人士)的反义词，意思为“业余人士，业余选手”。
\l "_Tc16775" 知识点4 推理判断题（作者态度/意图/隐含意义）
一、【命题解读】
推理判断题属于高层次阅读理解题，是指在理解原文字面意义的基础上,通过对语篇逻辑关系的分析和细节的暗示,作出一定的判断和推理,从而得出文章的深层意义及隐含意义的过程，主要考查考生理清上下文逻辑关系的能力以及考生的识别能力。
常以infer, imply, suggest, cnclude, learn, intend, mean, describe, purpse等词提问。
提问中含有表示推测的情态动词,如can, culd, might, wuld 等和其他表示可能性的副词,如prbably, pssibly等。
具体的设问方式如:
What can we infer frm the () paragraph?
Where des this passage prbably cme frm?
What’s the authr’s attitude
What is the main purpse f the passage?等
二、【解题策略】
正确选项推理判断题中的正确选项是依据文章的事实或证据推断出的符合逻辑的结论或观点,正确选项一般具有以下特征:
“立足原文,只推一步”,即根据原文内容,一步即可推得。
(2)选项中一般不可以出现绝对概念。如nly, never, all, abslutely等,正确答案的表述一般有一点模糊,会用一些相对能够留有一些余地的词汇,如ften, usually, smetimes, sme, may, might, can, culd, pssibly, prbably等。
干扰选项特征
三、【常考类型】
01深层推断题（隐含推断题）
推断隐含义类的题目要求考生根据文章中的信息(句子、 段落或者全文)进行合理的、适度的逻辑推断，推断出作者没有直接说明的内容、可能会发生的事情或者事务的特征等，理解作者的言外之意。这类题目特点如下：
01题干设置关键词
常用 infer, indicate, imply, suggest, cnclude, assume, knw, learn 等 动词以及 suggestin, indicatin, assumptin, cnclusin 等名词。
02正确选项的设置
(1) 根据原文推断出的内容，不是原文内容的直接体现，多为语块或词块 的同义词转化或者表层含义的延伸；
(2)一般不含有绝对概念的字眼，通常含有 usually, may, sme, prbably, be likely t, abut, can, pssibly, prbably 等。
03 常见的设问方式
What can we infer abut … frm the text?
What can we learn abut … ?
What can be inferred abut … ?
02 写作意图推断题-依据文体特点推断写作意图
常见设问方式：
What is the main purpse f the authr writing the text?
The writer f the stry wants t tell us that________
The fact... is mentined by the authr t shw______
The authr writes the last paragraph in rder t_____
解题技巧：
1. 关注设问的信息位置:
1)开头提出问题——T attract readers’ attentin；T intrduce the tpic;
2)开头举例，用谚语或者名人名言——T draw the readers’ attentin t the tp;
3)结尾设问——T attract readers t pay fr a trip t sme attractin; T call n…; T sell a prduct r service；
4)文中引语、事例、研究的数据和研究——T argue against…; T supprt ne’s wn idea; T make it mre persuasive (更有说服力的)
2. 关注逻辑关系和篇章结构（TEEC 模式）Tpic→explanatin→example→cnclusin 主题+举例子解释或者证明——T stress/cnfirm/supprt sth
常见的说明方法:
1、列数字(list figures): 具体而准确地说明该事物的特点。使说明更有说服力。
2、举例子(give examples): 具体真切地说明了事物的等等特点。
3、引资料(qute): 能使说明的内容更具体、更充实。用引用的方法说明事物的特征，增强说服力。如引用古诗文、谚语、俗话。引用说明在文章开头，还起到引出说明对象的作用。
4、分类别(by categry): 条理清楚地说明了事物的特点，对事物的特征/事理分门别类加以说明，使说明更有条理性。使说明的内容眉目清楚，避免重复交叉的现象。
5、打比方(make an analgy) : 打比方就是修辞方法中的比喻。生动形象地说明该事物的xx特点，增强了文章的趣味性。
6、下定义(draw a definitin/ make analysis): 用简明科学的语言对说明的对象/科学事理加以揭示，从而更科学、更本质、更概括地揭示事物的特征/事理。
7、作比较(make a cntrast/cmparisin): 突出强调了被说明对象的特点(地位、影响等)。
\l "_Tc17630" 考向1 考查主旨大意题
例1 (2025·北京·高考)
Nt t lng ag, n a cld winter night, there was a teenager wh wanted mre screen time and a parent wh said n. The teenager was advcating fr her right t scrll (翻屏) fr an extra 30 minutes. The parent argued that nne f her friends’ parents let them have screens after 9 ’clck. “I thught, in this family, we dn’t cmpare urselves with ther peple, Dad?” the teenager replied. The parent — wh was me, by the way — just gt served. Since they were yung, I have tld my kids nt t cmpare themselves with ther peple. I have argued cuntless times that cmparisns are the “thief f jy”.
Althugh my daughter didn’t win, she did help expse ne f the wrst pieces f advice I have ever given. In my defence, I did what we’ve all dne befre, which is repeat received wisdm withut explring the nuances. But nw is the time t set the recrd straight, which starts with questining the idea that all scial cmparisn is unhealthy.
Scial cmparisns d, f curse, ften get us int emtinal truble. But they can be harnessed (利用) fr ur betterment if we understand hw they wrk. The scial cmparisns we make — nes that lead us t feel gd r bad abut urselves — are vital t ur ability t thrive (成长). Science prvides a guide we can use t harness the way we perfrm these cmparisns t reduce their negative emtinal impacts.
Cmparing yurself with smene wh is utperfrming yu culd result in feelings f envy if yu fcus n the things they have and yu dn’t, r it can be energizing and inspiring if yu use these cmparisns as a surce f mtivatin, fr example, “If they can achieve that, s can I.” Cmparing yurself with smene wh is ding wrse than yu culd result in fear and wrry if yu think abut hw yu culd fall int similar circumstances, r it can draw ut feelings f gratitude and appreciatin if yu use that cmparisn t braden yur views — fr example, “Ww, things culd be much wrse; I’m ding great.”
What I wish I taught my daughter earlier are these nuances. Hw we feel abut urselves rests nt just n whm we cmpare urselves with but als n hw we think abut that cmparisn. That’s smething we all have cntrl ver.
30. Which wuld be the best title fr the passage?
A. Cmparing Ourselves with Others Can Becme a Healthy Habit
B. Cmparing Ourselves with Others Can Strengthen Family Ties
C. Scial Cmparisns Can Get Us int Emtinal Truble
D. Scial Cmparisns Can Be Cntrlled by Science
【变式训练1】（23-24高三上·北京石景山·期末）A Swiss radi statin recently carried ut a scial experiment n air, testing rbt-created vices and cntent. The 13-hur experiment tk place at the French-language statin Culeur 3. During the perid, listeners heard the clned vices f five human presenters. The statin’s prgramming als included music created by artificial intelligence (AI) methds. The prgramming infrmed listeners abut the experiment every 20 minutes.
“AI is taking yur favrite radi by strm,” a vice said. “Our vice clnes and AI are here t unsettle, surprise and shake yu. And fr that matter, this text was als written by a rbt.”
Recent AI develpments have led t the creatin f a series f tls that permit rbts t lead different human activities. These tls belng t a grup f systems knwn as “generative AI”. The tls use machine learning methds t train AI systems n huge amunts f data t prduce human-quality results. One f the mst highly publicized generative AI tls is called ChatGPT. It received wide attentin by demnstrating the ability t quickly prduce written answers t questins at a level and quality similar t humans. Hwever, the develpment f “generative AI” systems has led t sme criticism f the technlgy. Critics have warned that such systems, if used incrrectly, culd cause ecnmic, cultural and scial harms.
The statin said in a statement it received hundreds f messages n the day f the experiment, with sme supprting and thers ppsing. One persn cmplained f unfunny jkes. Anther listener admitted t nt recgnizing the prgramming as an experiment. One critic called the prject a waste f time fr a statin that gets public financing. Many listeners nted, “Yu can sense these are rbts, and there are fewer surprises, less persnality.” Sme listeners were even mre frceful, urging statin fficials t “give us back ur humans!”
The Swiss statin’s chief, Antine Multne, tld The Assciated Press that Culeur 3 was able t carry ut the experiment because it is already knwn fr ding prvcative things.
Multne defended the prject as a lessn n hw t live with AI. “I think if we becme striches (鸵鸟) ... we put ur heads in the sand and say, ‘Mn Dieu, there’s a new technlgy! We’re all ging t die!’ then yeah, we’re ging t die because it (AI) is cming, whether we like it r nt,” Multne said by phne. “We want t master the technlgy s we can then put limits n it.” He added that abut 90 percent f the listener reactins suggested the experiment was a gd idea.
4．Which wuld be the best title fr the passage?
A．Putting AI Vices n RadiB．Creating Generative AI Tls
C．Explring the Develpment f AID．Replacing Annuncers with AI
【变式训练2】 （24-25高三上·北京房山·期末）It was an arresting image: a large airplane parked n the Antarctic ice, smiling flight attendants psing in frnt f it. The sht seemed t mark a new phase fr Antarctic turism. Frtunately, the flight in questin was delivering staff and supplies fr research rather than turism — but travel t Earth’s suthernmst cntinent has nnetheless reached a new milestne. The grwing number f visitrs brught a new urgency t the questin f hw much turism shuld be allwed n the icy cntinent.
Many members f the Internatinal Assciatin f Antarctic Turism Operatrs (IAATO) say prmting Antarctic cnservatin is part f their missin. In fact, IAATO members fllw strict rules designed t prtect the envirnment, including remving all waste frm the cntinent and aviding unknwingly intrducing nn-native species. Nnetheless, a number f bisecurity studies fund a large number f nn-native species present. The risks are real. An invasive (侵袭的) grass species has established a fthld n ne f Antarctica’s Suth Shetland Islands, while bird flu recently reached the Sub-Antarctic Islands, where it has had a damaging effect n the seal ppulatin.
Despite these grwing threats, bisecurity is nt the biggest danger facing Antarctica’s wilderness areas. Turists can reduce bisecurity risks by taking new clthing t Antarctica, but the carbn impact is a real issue. The average per-persn carbn emissins (排放) fr an Antarctic turist are 3.76 tnnes — abut the ttal sum that an individual typically generates in an entire year. One study calculated that each turist between 2016 and 2020 was effectively melting arund 83 tnnes f snw, due largely t emissins frm travel ships.
The Antarctic is at risk nt just because f the delicacy f its envirnment, but due t the lack f a single gverning bdy. The Antarctic Treaty, established in 1961 t prvide gvernance fr the cntinent, perates n a cnsultative basis, which means all parties have t agree befre a change can be put int actin.
There is a cmmn understanding that smething needs t change, but n agreement n what thse changes shuld be. Shuld landings be made at a larger number f sites fr instance, r shuld we aim t keep the human ftprint as small as pssible? Researchers recmmend that anyne thinking abut visiting Antarctica shuld take a hard lk at their mtivatin and the impacts f their chice.
“As a researcher, it’s a mral decisin that I make every time I g, whether what I’m ding is wrth the impact,” said a prfessr, wh says turists shuld als weigh up the cnsequences.
“If yur mtivatin is simply because yu have stepped n six cntinents already and yu want t step n the seventh — persnally I think that’s a fairly silly reasn.”
4．Which wuld be the best title fr this passage?
A．Antarctic Research: A Prmising Future!
B．Flying t the Antarctic: A New Milestne!
C．Antarctic Turism: Shuld We Just Say N?
D．Threat t the Antarctic: Why Bisecurity Risks?
【变式训练3】 （2023·北京朝阳·二模）Superhuman artificial intelligence is already amng us. Well, srt f. When it cmes t playing games like chess and G, r slving difficult scientific challenges like predicting prtein structures, cmputers are well ahead f us. But we have ne superpwer they aren’t clse t mastering: mind reading.
Humans have a mysterius ability t reasn the gals, desires and beliefs f thers, a crucial skill that means we can anticipate ther peple’s actins and the cnsequences f ur wn. Reading minds cmes s easily t us, thugh, that we ften dn’t think t spell ut what we want. If AIs are t becme truly useful in everyday life—t cperate effectively with us r t understand that a child might run int the rad after a buncing ball—we have t give them this gift that evlutin has given us t read ther peple’s minds.
Psychlgists refer t the ability t infer anther’s mental state as thery f mind. In humans, this capacity starts t develp at a very yung age. Hw t reprduce the capability in machines is far frm clear, thugh. One f the main challenges is cntext. Fr instance, if smene asks whether yu are ging fr a run and yu reply “it’s raining”, they can quickly cnclude that the answer is n. But this requires huge amunts f backgrund knwledge abut running, weather and human preferences.
Mrever, whether humans r AI, the thery f mind is suppsed t emerge naturally frm ne’s wn learning prcess. Building prir knwledge int AI makes it reliant n ur imperfect understanding f thery f mind. In additin, AI may be capable f develping appraches we culd never imagine. There can be many frms f thery f mind that we dn’t knw abut simply because we live in a human bdy that has certain types f senses and a certain ability t think.
Yet we might still want AI t have a mre human-like frm f thery f mind. Humans can clearly explain their gals and desires t each ther using cmmn language and ideas. While letting AI frm the thery f mind in their learning prcess is likely t lead t develping mre pwerful AI, plainly building in shared ways t represent knwledge may be crucial fr humans t trust and cmmunicate with AI.
It is imprtant t remember, thugh, that the pursuit f machines with thery f mind is abut mre than just building mre useful rbts. It is als a stepping stne n the path twards a deeper gal fr AI and rbtics research: building truly self-aware machines. Whether we will ever get there remains t be seen. But alng the way thinking abut ther peple and ther agents, we are n the path t learning t think abut urselves.
35．Which wuld be the best title fr the passage?
A．AI with Its Own Thery f Mind Is Expected
B．AI with Thery f Mind Will Reshape Our Future
C．AI’s Thery f Mind Is a Blessing r Suffering t Humans
D．Thery f Mind Bridges the Gap Between Humans and AI
\l "_Tc16322" 考向2 考查词义猜测题
例1 (2024·北京·高考片段) The ntin that we live in smene else’s vide game is irresistible t many. Searching the term “simulatin hypthesis” (模拟假说) returns numerus results that debate whether the universe is a cmputer simulatin —— a cncept that sme scientists actually take seriusly. Unfrtunately, this is nt a scientific questin. We will prbably never knw whether it’s true. We can, instead, use this idea t advance scientific knwledge.
The 18th-century philspher Kant argued that the universe ultimately cnsists f things-in-themselves that are unknwable. While he held the ntin that bjective reality exists, he said ur mind plays a necessary rle in structuring and shaping ur perceptins. Mdern sciences have revealed that ur perceptual experience f the wrld is the result f many stages f prcessing by sensry systems and cgnitive (认知的) functins in the brain. N ne knws exactly what happens within this black bx. If empirical (实证的) experience fails t reveal reality, reasning wn’t reveal reality either since it relies n cncepts and wrds that are cntingent n ur scial, cultural and psychlgical histries. Again, a black bx.
29. What des the phrase “cntingent n” underlined in Paragraph 2 prbably mean?
A. Accepted by.B. Determined by.C. Awakened by. D. Discvered by.
【变式训练1】（23-24高三上·北京丰台·期末）Over millins f years humans have respnded t certain situatins withut thinking t hard. If ur ancestrs sptted mvement in the nearby frest, they wuld run first and questin later. At the same time, the ability t analyze and t plan is part f what separates us frm ther animals. The questin f when t trust yur instinct (直觉)and when t think slw matters in the ffice as much as in the savannah(草原).
Slw thinking is the feature f a well-managed wrkplace. Yet instinct als has its place. Sme decisins are mre cnnected t emtinal respnses and less t analysis. In demanding custmer-service r public-facing situatins, instinct is ften a better guide t hw t behave.
Instinct can als be imprved. Plenty f research has shwn that instinct becmes mre unerring with experience. In ne well-knwn experiment, vlunteers were asked t assess whether a selectin f designer handbags were real r nt. Sme were instructed t perate n instinct and thers t deliberate(深思熟虑)ver their decisin. Instinct wrked better fr thse wh wned at least three designer handbags; indeed, it utperfrmed analysis. The mre expert yu becme, the better yur instinct tends t be.
Hwever, the real reasn t embrace fast thinking is that it is, well, fast. It is ften the nly way t get thrugh the day. T take ne example, when yur inbx flds with new emails at the start f a new day, there is abslutely n way t read them all carefully. Instinct is what helps yu decide which nes t answer and which t delete r leave unpened. Fast thinking can als help the entire rganizatin. The value f many managerial decisins lies in the simple fact that they have been made at all. Yet as data expldes, the temptatin(诱惑)t ask fr ne mre bit f analysis has becme much harder t resist. Managers ften suffer frm verthinking, turning a simple prblem int a cmplex ne.
When t use instinct in the wrkplace rests n its wn frm f pattern recgnitin. Des the decisin maker have real expertise in this area? Is this a field in which emtin matters mre than reasning? Abve all, is it wrth delaying the decisin? Slw thinking is needed t get the big calls right. But fast thinking is the way t stp deliberatin turning t a waste f time.
1．What des the underlined wrd “unerring” in Paragraph 3 prbably mean?
A．Accurate.B．Creative.C．Cntrllable.D．Obvius.
【变式训练2·变考法】
（24-25高三上·北京海淀·期中）Science shuld guide plicy, but cautin is needed when technlgies like CRISPR have the ptential t exclude rather than assist peple t live their lives.
CRISPR can be used t treat disease. Yet the far-reaching, mre wrrying prmise f this technlgy — ne abut which scientists seem at nce excited and cautius — lies in its ability t eliminate frm the gene pl what medical science identifies as faulty r abnrmal genes that cause difference in individual peple. Certainly, ges the lgic f CRISPR’s prmise, the gal f ridding future generatins f terrible diseases that cause suffering and death and exhaust resurces, seems an unquestinable enterprise.
This lgic is cnsistent with wider scietal views. The idea that ridding sciety f genetic differences that cunt as defect (缺陷) is an undeniable “gd” cntinues t be pervasive. Editing ut a gene-linked cnditin, supprters may argue, is different frm editing ut a persn, and curing disease is a gd thing. But ur genetic cnditins are nt simply entities that can be clipped away frm us and ur genetic cnditins frm a fundamental part f wh we are.
Using genme manipulatin tls and perfrming genetic selectin is equal t a new frm f eugenic (优生学的) thinking grunded in what the cmmunicatins studies schlar James L. Chemey calls “cmmn sense” ableism, a belief system that allws peple t simultaneusly deny any cmmitment t distasteful eugenic principles while als hlding them up. Ultimately, “cmmn sense” ableism embdies a similar gal f cleansing unacceptable human variatins that the campaigns t eliminate the suppsedly inferir have held in the past.
Peple with “bad” genes shuldn’t be edited ut f existence in sme versin f a utpian (乌托邦的) future. Evaluating the quality f life f anther persn is mrally questinable in a sciety based n the cncept that all peple are f equal value regardless f their individual differences.
Expanding diversity in all its frms, including disability, strengthens the human cmmunity ethically and bilgically because it pens the public and private sphere t a variety f perspectives, experiences and ideas t live tgether with mutual flurishing.
Genme editing is pwerful in reshaping medical treatments, but it can als be harmful by editing ut the kinds f peple that medical science, and the sciety it has shaped, categrize as diseased r genetically cntaminated — peple wh are understd as having bad genes. We shuld be reminded that bad genes dn’t necessarily lead t bad lives, just as gd genes dn’t necessarily lead t gd lives. If CRISPR is put t use t eliminate rather than t treat genetic difference, we as a sciety wuld essentially instrumentalize this mralistic and reductinist assumptin.
3．What des the underlined part in the last sentence mean?
A．Peple need t adpt mre simplified slutins.
B．The sciety shuld apply CRISPR int ther fields.
C．This belief might be widely practiced in the sciety.
D．The technlgy will be used t create a utpian future.
考向3 考查推理判断题之深层推理
例1(2025·北京·高考) Nt t lng ag, n a cld winter night, there was a teenager wh wanted mre screen time and a parent wh said n. The teenager was advcating fr her right t scrll (翻屏) fr an extra 30 minutes. The parent argued that nne f her friends’ parents let them have screens after 9 ’clck. “I thught, in this family, we dn’t cmpare urselves with ther peple, Dad?” the teenager replied. The parent — wh was me, by the way — just gt served. Since they were yung, I have tld my kids nt t cmpare themselves with ther peple. I have argued cuntless times that cmparisns are the “thief f jy”.
Althugh my daughter didn’t win, she did help expse ne f the wrst pieces f advice I have ever given. In my defence, I did what we’ve all dne befre, which is repeat received wisdm withut explring the nuances. But nw is the time t set the recrd straight, which starts with questining the idea that all scial cmparisn is unhealthy.
Scial cmparisns d, f curse, ften get us int emtinal truble. But they can be harnessed (利用) fr ur betterment if we understand hw they wrk. The scial cmparisns we make — nes that lead us t feel gd r bad abut urselves — are vital t ur ability t thrive (成长). Science prvides a guide we can use t harness the way we perfrm these cmparisns t reduce their negative emtinal impacts.
Cmparing yurself with smene wh is utperfrming yu culd result in feelings f envy if yu fcus n the things they have and yu dn’t, r it can be energizing and inspiring if yu use these cmparisns as a surce f mtivatin, fr example, “If they can achieve that, s can I.” Cmparing yurself with smene wh is ding wrse than yu culd result in fear and wrry if yu think abut hw yu culd fall int similar circumstances, r it can draw ut feelings f gratitude and appreciatin if yu use that cmparisn t braden yur views — fr example, “Ww, things culd be much wrse; I’m ding great.”
What I wish I taught my daughter earlier are these nuances. Hw we feel abut urselves rests nt just n whm we cmpare urselves with but als n hw we think abut that cmparisn. That’s smething we all have cntrl ver.
28. Hw did the authr feel abut his daughter’s argument?
A. Excited.B. Inspired. C. Energized.D. Relieved.
【变式训练2】（24-25高三上·北京海淀·期末）Crucial systems acrss the wrld cllapsed n Friday, triggered by ne mistake in a single cmpany. The pwer cut f CrwdStrike, a giant in the cybersecurity industry, hit banks, airlines, and health-care systems. It may end up being the wrst infrmatin technlgy disaster in histry.
This was nt, hwever, an unfreseeable freak accident, nr will it be the last f its kind. Instead, the devastatin was the inevitable utcme f mdern scial systems that have been designed fr hypercnnected ptimizatin (优化), nt decentralized resilience.
There is ften a trade-ff between maximum ptimizatin and resilience. Cnsider a basic prehistrical scial system in which many humans lived in small, islated bands. What any single persn did wuld have little t n effect n thse living elsewhere. It was an inefficient, basic system — but if ne part f the human system failed, few thers were affected.
Thrughut ur advancement as a species, frm building empires t building machines, scial systems have evlved t be mre cnnected and centralized. In the 21st century, the cmbinatin f glbalizatin and digitizatin has created a landscape characterized by the threat f catastrphic, immediate risk. Glbalizatin enables large efficiency gains, where a prduct can be put tgether frm carefully managed links in the glbal supply chain. But thse systems lack resilience. Every link must fit tgether perfectly; the system falls apart if even ne chain breaks.
Centuries ag, the philspher David Hume wrte that we can never be certain that the patterns f the past will remain the patterns f the future. This is especially true in the 21st century as we are betting mre and mre f ur wrld n unstable systems. Can we really trust ur species t flawlessly gvern unimaginably cmplex systems — systems we dn’t always fully understand — that can be brught dwn by a single screw-up?
Mdern scieties have discunted the cst f that risk because ur current reward systems favr ptimizatin ver resilience. CEOs try t deliver shrt-term imprvements, nt lng-term planning. Nbdy gets reelected by investing in a rainy-day fund. Business leaders are hired r fired based n quarterly results.
Even thugh the mdern quest fr ptimizatin has t ften made resilience an afterthught, it is nt inevitable that we cntinue dwn the risky path we’re n. And making ur systems mre resilient desn’t require ging back t a discnnected, primitive wrld, either. Instead, ur cmplex, intercnnected scieties simply demand that we cmprmise a bit f efficiency in rder t allw a little extra cushin (缓冲).
If scial systems rely n a mre diverse digital array f cmpanies, the scieties will be less vulnerable (脆弱的). Fr the brader set f risks facing glbal sciety, better regulatin is essential t ensure fail-safes, backups, and stress testing — s that ne errr desn’t bring dwn entire systems. The CrwdStrike breakdwn is a clear warming that the mdern wrld is fragile by design. S far, we have decided t make urselves vulnerable. That means we can decide differently t.
1．The authr mentins the CrwdStrike mainly t ______.
A．highlight its crucial rle in cybersecurityB．intrduce an infrmatin technlgy cllapse
C．explain why peple are in a cnnected systemD．indicate predictable failures f the mdern system
2．What can be inferred abut a mre glbalized system?
A．It leads t a highly advanced digitalized netwrk.
B．It tends t priritize adaptability ver prductivity.
C．It enlarges the impact f lcal errrs t a glbal scale.
D．It guarantees immediate ecnmic rewards fr participants.
3．As fr the future f scial systems, which wuld the authr agree with?
A．The mre discnnected they are, the mre resilient they will be.
B．Sme efficiency shuld be sacrificed fr mre flexibility.
C．Lng-term planning prevents structural breakdwns.
D．Histry can help peple predict future patterns.
【变式训练3】（2025年·海淀·二模） If yu’ve ever hung arund scientists, yu’ve mst likely heard ne f them say “the best explanatin is the simplest ne.” But is it? Frm the behavir f ants t the ccurrence f trnades, the natural wrld is ften quite cmplex. Why shuld we assume the simplest explanatin is clsest t the truth?
This idea is knwn as Occam’s (r Ockham’s) razr. It’s als referred t as “rule f ecnmy”. And it bears a family relatinship t the “principle f least astnishment,” which hlds that if an explanatin is t surprising, it’s prbably nt right. The name cmes frm William f Ockham, a 14th-century schlastic philspher. He frmulated the principle that “entities (实体) shuld nt be multiplied beynd necessity.” The philsphical claim is a frm f ntlgical minimalism: we shuld nt invke entities unless we have evidence that they exist. In ther wrds: dn’t make stuff up.
In 1687, Isaac Newtn expanded n the ntin with his cncept f a vera causa — a true cause, stating that we shuld admit nly causes that were bth true and sufficient t explain natural phenmena. He added that Nature did nthing in vain and Nature was pleased with simplicity. Althugh Newtn was a great scientist, this claim seems dd. Wh is t say what “pleases Nature”? Desn’t this guidance assume we knw what we are in fact trying t figure ut?
Cnsider the wrld f Physics filled with explanatins that are surprising, unexpected and hard t get yur head arund. Newtn explained light as being made f particles, whereas ther scientists explained it as a wave. Quantum mechanics, hwever, tells us light is bth a wave and a particle. Newtn’s accunt was simpler, but mdern physics tells us the mre cmplex mdel is clser t the truth.
When we turn t bilgy, things get even mre cmplicated. Imagine tw smkers, bth f whm went thrugh a pack a day fr 30 years. One gets cancer; the ther desn’t. The simplest explanatin? Fr decades the tbacc industry’s answer was that smking desn’t cause cancer. Simple but false. In fact, disease is cmplex, and we dn’t yet understand all the factrs invlved in cancer.
Occam’s razr is nt a fact r even a thery. It’s a metaphysical (形而上学的) principle: an idea held independently f empirical (实证的) evidence. In human affairs, things are mre ften than nt cmplex. Human mtivatins are typically multiple. Peple can be gd and bad at the same time, selfish and selfless, depending n circumstances. The shelves f ethicists are filled with bks pndering why gd peple d bad things, and their answers are rarely shrt and sweet.
Our explanatins shuld match the wrld as best as we can make them. Science is abut allwing things t unfld naturally, and smetimes this means accepting that the truth is nt simple, even if it wuld make ur lives easier if it were.
28. Occam’s razr indicates that_________.
A. simpler explanatins shuld be preferred
B. reasnable explanatins can’t be surprising
C. explanatins shuld be cnsistent with purpses
D. sufficient causes can explain natural phenmena
29. What can we learn frm this passage?
A. Newtn ffered slid empirical supprt t Occam’s razr.
B. The tbacc industry’s respnse is in line with Occam’s razr.
C. Quantum mechanics cnfirms Newtn’s particle thery f light.
D. Ethicists argue human cmplexity results in multiple mtivatins.
30. It’s implied in the passage that we need t ________.
A. fllw the laws f natureB. interpret the wrld as it is
C. balance accuracy and simplicityD. highlight the existence f entities

考点二 议论文中低频考点
知识点1 细节理解题（事实信息定位）
1. 细节理解题（事实信息定位）
出现频率：偶尔出现，通常为1题
常见问法：
Accrding t the passage, what can we learn
解题关键：精准定位原文，避免主观推断。
知识点2 推理判断题（观点态度/结构功能/文章出处/人物性格/目标读者/后续走势）
01 观点态度题-利用语境的褒贬性进行信息推断
推断作者或者文中人物的意图态度题就是指针对文章作者或者文中人物对某事物所持的观点或者态度进行设问。文章作者或文中人物对某事物所持的情感、观点或态度往往隐含在文章的字里行间或流露于修饰词之中。
观点态度类的题目旨在考在考生对作者或者文中人物的观点或态度的理解能力，要求考生在理解文意的基础上进行推理判断。有时文中没有直接表明相关人物的观点态度的词句，需要考生结合文中描述该人物的相关词句及他人的评价等信息来推断其观点或态度。
常见的设问方式有:
Hw des the authr feel
What des the authr think
What is the authr’s attitude
题干设置关键词attitude, pinin, believe, cnsider, regard, feel abut, think f 等。
作者的观点和态度一般分为三大类：乐观、支持、赞同；中立、客观；悲观、怀疑、反对、批评。作者的
思想态度往往隐含在文章的字里行间，应特别注意文中表达感情色彩的形容词和副词。
【观点态度词】
一、表中立，客观
二、表赞同、支持
三、表怀疑、否定
四、其他
02目标读者推断题-利用语言表达方式或者文章的内容
推断文章作者或目标读者的设题方式：
Wh prbably wrte the letter?
Wh is the passage written fr?
Wh are the intended readers f the passage?
The authr prbably writes this passage fr __________.
阅读理解中通常会要求考生根据文章的内容来判断文章作者或目标读者。
1: 判断文章的作者
判断文章作者，可以从语言表达方式或者文章的内容两个方面去判断。
例如：如果材料是议论文，考生可以留意一下作者对于论点所持有的态度或观点，这些态度或观点是作者直接表述的，还是引用别人的。如果材料是说明文，那么作者很可能就是与材料相关的专业人士。
2: 推断文章的目标读者
文章的目标读者，也就是文章的指向性。此类题目与文字的内容是紧密相连的。不同的文章内容，其目标读者是不同的。
核心原则
内容决定读者：文章讨论的核心议题和论点，决定了谁会对它感兴趣。
语言反映读者：作者的措辞、语气和论据类型，是为目标读者“量身定制”的。
来源暗示读者：文章出处（如杂志、网站）是最直接的线索。
二、 具体分析步骤（“四看”法）
引导学生遇到此类题目时，依次从以下四个角度寻找证据：
一看：看议题与论点 (Lk at the Tpic & Argument)
问自己：这篇文章主要在讨论什么？作者想证明什么观点？
二看：看语言与论据 (Lk at the Language & Evidence)
专业术语多？→ 读者有专业知识背景（如科学家、学者）。
术语有解释，语言通俗？→ 读者是普通大众。
论据是数据、研究？→ 说服理性、重证据的读者。
论据是个人故事、情感呼吁？→ 说服更易共鸣的普通读者。
三看：看来源与体裁 (Lk at the Surce & Genre)
这是最快、最准的线索！务必提醒学生注意文章标注的出处。
A business magazine -> 读者：商人、投资者
An academic jurnal -> 读者：学者、学生
An educatin blg -> 读者：家长、老师
无明确来源，但体裁是Opinin piece（观点文章） -> 读者：关心社会议题的普通公众
四看：看作者口吻与呼告 (Lk at the Tne & Direct Address)
作者在用什么样的身份对读者说话？
“We as a sciety...” -> 呼吁社会公众
“Parents shuld cnsider...” -> 直接呼告家长群体
“Fr researchers...” -> 明确指向研究人员
3. 解题流程与误区警告
答题步骤：
1）审题：明确题目问的是“intended audience”（目标读者）。
2）速览：重点读标题、出处、首段和尾段，形成初步假设。
3）验证：带着假设精读，用“四看”法寻找证据。
4）排除：排除那些与文章内容无关、语言风格不符或范围绝对化（如“all peple”）的选项。
5）选择：选出与所有线索最匹配的选项。
常见误区：
❌ 过度推断：文章只讨论了教育的一个方面，不能推断读者是“all teachers”。
❌ 主观臆断：不能因为“我觉得感兴趣”，就选“high schl students”。
✅ 正确做法：所有结论必须源于文本证据。
03 结构功能题（段落/句子作用）
常见问法：
Why des the authr
What is the functin f Paragraph X?
解题关键：分析段落与全文逻辑关系（举例、对比、引出话题等）。
04 推断文章出处-利用文体特征进行推断
推断文章出处或类别要从文章内容、语言特色和标志信息着手;确定读者对象要根据文章主题和文章措辞来判断。
常见设问方式：
This passage wuld be mst likely t be fund in____
The passage is prbably taken ut f _____________
Where des this text prbably cme frm?
Which sectin f a magazine is this passage prbably taken frm?
判断文章出处的题目应从文章的体裁和内容着手。一般来说，报纸上的新闻前面会出现日期、地点或通讯社名称等；广告类文章因其格式特殊，容易辨认；产品说明类文章如器皿、设备的使用说明会有产品名称或操作方式，而药品的服用说明会告知服用时间、次数、药量等；来自网络的文章一般比较新颖，时效性强。
Newspaper特征：1.首段或首句为新闻归纳(时效性强)。2.有特殊的文体标示(如Reuters)
Magazine 特征：1.内容更加丰富，专题性更强，话题更详细语言轻松活泼、语言表述更具作者的主体意识。
Research特征：1. 语言比较专业化，会有一些专业词汇(如Planetary rbit)2.内容上严肃且精华，经常出现专业的知识。
常见选项：
A bilgy textbk(生物教科书)/A magazine /A research paper(研究论文) /A travel brchure(旅游手册)/A news reprt(新闻报告)/A bklet(小册子)/A website/a blg(博客；网络日志)/ A guide bk/An advertisement 等.
05 判断人物性格特征题
推断人物特征类的题目主要考查考生对文章中出现的人物性格行为特征等进行综合分 析和推断的能力。考生要特别关注有关人物的语言、情感、行为等语句及文中涉及该人物的 具体的事实信息，充分利用表达感情色彩、态度观点的词汇推断人物特征。
01常见的设问方式
What can we say abut sb. ?
What can we learn abut sb. ?
What kind f persn is sb. ?
What can best describe ...？
02描写人物特征的相关形容词:
adaptable 有适应能力的 aggressive 有进取心的 ambitius 有雄心的 appreciative 感激的，感谢的 4cautius 小心的，谨慎的 cncerned 关心的 cnsiderate 体贴的 cperative 合作的 curageus 勇敢的 demanding 要求极严的 devted 全心全意的 reliable 可靠的faithful 忠诚的 far-sighted 有远见的 humrus 幽默的 independent 独立的 indifferent 冷漠的 diligent勤奋的 respnsible负责任的 knwledgeable知识渊博的 sensible 明智的，理智的 straightfrward 率直的，坦率的 talented 有才能的 tlerant 宽容的，容忍的 innvative 创新的 easy-ging 随和的 pen-minded 思想开明的 grateful 感激的，感谢的 intelligent 聪明的 determined 意志坚定地 cmmitted 尽心尽力的
06 后续走势和篇章结构题
后续情节发展推断题要求考生对接下来的故事情节或文章内容进行推断。解答该题型时，考生要把握作者的写作思路，分析段落之间的联系，继而作出合理的推断。
常见的设问方式：
1. What will be discussed further in the cming paragraph?
2．What may the researchers d next accrding t the last paragraph?
3．What wuld the authr mst prbably discuss next?
4．Where des the article g next?
5.What wuld the fllwing paragraph talk abut?
考向1 考查细节理解题
例1 (2022·北京·高考) Quantum ( 量子 ) cmputers have been n my mind a lt lately. A friend has been sending me articles n hw quantum cmputers might help slve sme f the biggest challenges we face as humans. I’ve als had exchanges with tw quantum-cmputing experts. One is cmputer scientist Chris Jhnsn wh I see as smene wh helps keep the field hnest. The ther is physicist Philip Taylr.
Fr decades, quantum cmputing has been little mre than a labratry curisity. Nw, big tech cmpanies have invested in quantum cmputing, as have many smaller nes. Accrding t Business Weekly, quantum machines culd help us “cure cancer, and even take steps t turn climate change in the ppsite directin.” This is the srt f hype ( 炒作 ) that annys Jhnsn. He wrries that researchers are making prmises they can’t keep. “What’s new,” Jhnsn wrte, “is that millins f dllars are nw ptentially available t quantum cmputing researchers.”
As quantum cmputing attracts mre attentin and funding, researchers may mislead investrs, jurnalists, the public and, wrst f all, themselves abut their wrk’s ptential. If researchers can’t keep their prmises, excitement might give way t dubt, disappintment and anger, Jhnsn warns. Lts f ther technlgies have gne thrugh stages f excitement. But smething abut quantum cmputing makes it especially prne t hype, Jhnsn suggests, perhaps because “‘quantum’ stands fr smething cl yu shuldn’t be able t understand.” And that brings me back t Taylr, wh suggested that I read his bk Q fr Quantum.
After I read the bk, Taylr patiently answered my questins abut it. He als answered my questins abut PyQuantum, the firm he c-funded in 2016. Taylr shares Jhnsn’s cncerns abut hype, but he says thse cncerns d nt apply t PyQuantum.
The cmpany, he says, is clser than any ther firm “by a very large margin ( 幅度 )” t building a “useful” quantum cmputer, ne that “slves an impactful prblem that we wuld nt have been able t slve therwise.” He adds, “Peple will naturally discunt my pinins, but I have spent a lt f time quantitatively cmparing what we are ding with thers.”
Culd PyQuantum really be leading all the cmpetitin “by a wide margin”, as Taylr claims? I dn’t knw. I’m certainly nt ging t advise my friend r anyne else t invest in quantum cmputers. But I trust Taylr, just as I trust Jhnsn.
32. What leads t Taylr’s ptimism abut quantum cmputing?
A. His dminance in physics.B. The cmpetitin in the field.
C. His cnfidence in PyQuantum.D. The investment f tech cmpanies.
【变式训练1】 （2025年·东城·一模片段） Years after my art histry class, I am insufferable at museums. “That’s definitely a Matisse,” I say. “Yu can telI because f the brushwrk and the use f clur.” Smetimes it is nt a Matisse but ftentimes it is.
It is unsettling t learn, then, that fr all f my carefully wn art appreciatin, I am in danger f being surpassed by an insect. In a recent study, hneybees — whse brains are the size f grass seeds — were shwn Picasss and Mnets paired side by side. Belw the prints were tw small cntainers, ne cntaining sugar water and the ther nthing at all.
Which t enter? Bees culdn’t see r smell whether a given cntainer held the treat until they’d already flwn inside it. But they culd let the masterpieces guide them: fr sme bees, the reward was always under the Picass, while fr the rest it was under the Mnet. Over the curse f many trials, the bees learned t fly straight fr the crrect cntainer. Indeed, they even perfrmed slightly better than chance when faced with pairs f paintings they’d never seen befre. The bees had learned t discriminate, hwever mdestly, between the tw artists’ styles.
31. Why des the authr mentin bees?
A. T present an example.B. T put frward a thery.
C. T draw ut a cmparisn.D. T highlight a research finding.
【答案】31. C
【变式训练2】（2024高三上·北京石景山·期末片段）A Swiss radi statin recently carried ut a scial experiment n air, testing rbt-created vices and cntent. The 13-hur experiment tk place at the French-language statin Culeur 3. During the perid, listeners heard the clned vices f five human presenters. The statin’s prgramming als included music created by artificial intelligence (AI) methds. The prgramming infrmed listeners abut the experiment every 20 minutes.
“AI is taking yur favrite radi by strm,” a vice said. “Our vice clnes and AI are here t unsettle, surprise and shake yu. And fr that matter, this text was als written by a rbt.”
1．What did the scial experiment test?
A．Audiences’ feedback.B．Rbt-created systems.
C．Human presenters’ vices.D．AI-generated prgrammes.
考向2 考查推理判断题
例1(2024·北京·高考) The ntin that we live in smene else’s vide game is irresistible t many. Searching the term “simulatin hypthesis” (模拟假说) returns numerus results that debate whether the universe is a cmputer simulatin —— a cncept that sme scientists actually take seriusly. Unfrtunately, this is nt a scientific questin. We will prbably never knw whether it’s true. We can, instead, use this idea t advance scientific knwledge.
The 18th-century philspher Kant argued that the universe ultimately cnsists f things-in-themselves that are unknwable. While he held the ntin that bjective reality exists, he said ur mind plays a necessary rle in structuring and shaping ur perceptins. Mdern sciences have revealed that ur perceptual experience f the wrld is the result f many stages f prcessing by sensry systems and cgnitive (认知的) functins in the brain. N ne knws exactly what happens within this black bx. If empirical (实证的) experience fails t reveal reality, reasning wn’t reveal reality either since it relies n cncepts and wrds that are cntingent n ur scial, cultural and psychlgical histries. Again, a black bx.
S, if we accept that the universe is unknwable, we als accept we will never knw if we live in a cmputer simulatin. And then, we can shift ur inquiry frm “Is the universe a cmputer simulatin?” t “Can we mdel the universe as a cmputer simulatin? ” Mdelling reality is what we d. T facilitate ur cmprehensin f the wrld, we build mdels based n cnceptual metaphrs (隐喻) that are familiar t us. In Newtn’s era, we imagined the universe as a clck. In Einstein’s, we uncvered the standard mdel f particle (粒子) physics.
Nw that we are in the infrmatin age, we have new cncepts such as the cmputer, infrmatin prcessing, virtual reality, and simulatin. Unsurprisingly, these new cncepts inspire us t build new mdels f the universe. Mdels are nt the reality, hwever. There is n pint in arguing if the universe is a clck, a set f particles r an utput f cmputatin. All these mdels are tls t deal with the unknwn and t make discveries. And the mre tls we have, the mre effective and insightful we can becme.
It can be imagined that cmparable t the prcess f building previus scientific mdels, develping the “cmputer simulatin” metaphr-based mdel will als be a hugely rewarding exercise.
28. What des the authr intend t d by challenging a hypthesis?
A. Make an assumptin.B. Illustrate an argument.
C. Give a suggestin.D. Justify a cmparisn.
30. As fr Kant’s argument, the authr is _________.
A. appreciativeB. dubtful C. uncncerned D. disapprving
31. It is implied in this passage that we shuld _________.
A. cmpare the current mdels with the previus nes
B. cntinue explring the classical mdels in histry
C. stp arguing whether the universe is a simulatin
D. turn simulatins f the universe int realities up.
【变式训练1】
（2024高三上·北京石景山·期末）A persn culd be frgiven fr believing 20 years ag that the Internet wuld sn revlutinise academic publishing, because it became pssible fr publishers t spread schlarly wrk at the click f a buttn — much cheaper than the traditinal subscriptin-based (订阅) mdel. Recgnising the pprtunity, many schlars and librarians began t advcate a new, pen access mdel, in which articles are made freely available nline t anyne. The result wuld be a true nline public library f science.
Hwever, mre than tw decades later, the mvement has made nly slight prgress, and the traditinal subscriptin-based mdel remains entrenched.
Frtunately, things are changing. A big she drpped when the University f Califrnia (UC) Libraries, ne f the biggest library systems, declined t renew its cntract with Elsevier, a leading scientific publisher. Elsevier wanted the Libraries t pay tw fees: One fr its package f licensed jurnals and the ther fr the use f Elsevier’s pen access mdel. UC Libraries wanted the licensed jurnals fee t cver the pen access fee; they als wanted pen access t all UC researches published in Elsevier jurnals. When the tw sides culdn’t cme t terms, the Libraries walked away.
Actually, the pen access revlutin is mre likely t be led by research funding agencies, wh can use their purse pwer t prmte pen access. A team f funders, Calitin S, insisted that any research they fund shuld be published in a jurnal that makes all f its articles freely and immediately available t the public, which is called Plan-S.
Nw that sme librarians and funders are flexing their muscles, what shuld academics d? The wrst respnse wuld be t cmplain that Plan-S deprives(剥夺) them f academic freedm. Sme thughtful academics might wrry that a shift t pen access wuld affect their prmtin. After all, subscriptin jurnals are mre familiar and mre prestigius (有威望的) in the current system. Hwever, if enugh academics supprt pen access, the system culd reach a tipping pint beynd which subscriptins n lnger signal prestige. Reaching that pint wuld take cnsiderable time and effrts, but it is pssible.
When the jurnal system began in 1665, it was kind f a frm f pen access. Jurnals allwed academics t learn penly frm ne anther. It was nly in the 1900s that the jurnal system became thrughly cmmditized(商品化). Nw is the time t bring it back t its rts.
2．What is the cre f failed negtiatin between UC Libraries and Elsevier?
A．The duratin f the cntract.B．The way f payment.
C．The charge fr pen access mdel.D．The chice f licensed jurnals.
3．What can be inferred frm the passage?
A．Academics welcme pen access mdel with full heart.
B．Open access mdel will sn achieve a dminant psitin.
C．Publishers are willing t abandn the subscriptin mdel gradually.
D．Establishing a true nline public library f science requires jint effrts.
4．What is the authr’s attitude twards the pen access mdel?
A．Critical.B．Supprtive.C．Disapprving.D．Indifferent.
【变式训练2】
（2024高三上·北京丰台·期末）Over millins f years humans have respnded t certain situatins withut thinking t hard. If ur ancestrs sptted mvement in the nearby frest, they wuld run first and questin later. At the same time, the ability t analyze and t plan is part f what separates us frm ther animals. The questin f when t trust yur instinct (直觉)and when t think slw matters in the ffice as much as in the savannah(草原).
Slw thinking is the feature f a well-managed wrkplace. Yet instinct als has its place. Sme decisins are mre cnnected t emtinal respnses and less t analysis. In demanding custmer-service r public-facing situatins, instinct is ften a better guide t hw t behave.
Instinct can als be imprved. Plenty f research has shwn that instinct becmes mre unerring with experience. In ne well-knwn experiment, vlunteers were asked t assess whether a selectin f designer handbags were real r nt. Sme were instructed t perate n instinct and thers t deliberate(深思熟虑)ver their decisin. Instinct wrked better fr thse wh wned at least three designer handbags; indeed, it utperfrmed analysis. The mre expert yu becme, the better yur instinct tends t be.
Hwever, the real reasn t embrace fast thinking is that it is, well, fast. It is ften the nly way t get thrugh the day. T take ne example, when yur inbx flds with new emails at the start f a new day, there is abslutely n way t read them all carefully. Instinct is what helps yu decide which nes t answer and which t delete r leave unpened. Fast thinking can als help the entire rganizatin. The value f many managerial decisins lies in the simple fact that they have been made at all. Yet as data expldes, the temptatin(诱惑)t ask fr ne mre bit f analysis has becme much harder t resist. Managers ften suffer frm verthinking, turning a simple prblem int a cmplex ne.
When t use instinct in the wrkplace rests n its wn frm f pattern recgnitin. Des the decisin maker have real expertise in this area? Is this a field in which emtin matters mre than reasning? Abve all, is it wrth delaying the decisin? Slw thinking is needed t get the big calls right. But fast thinking is the way t stp deliberatin turning t a waste f time.
2．What can we learn frm the passage?
A．Managers can affrd the cst f slw thinking.
B．Fast thinking can be a bst t wrk efficiency.
C．Slw thinking will hld us back in the lng run.
D．T much data is t blame fr wrng decisins.
3．What is the authr's purpse f writing the passage?
A．T explain hw instinct wrks.
B．T cmpare instinct and slw thinking.
C．T highlight the value f instinct in the wrkplace.
D．T illustrate the develpment f different thinking patterns.
04真题溯源·考向感知
(2025·北京·高考)
Nt t lng ag, n a cld winter night, there was a teenager wh wanted mre screen time and a parent wh said n. The teenager was advcating fr her right t scrll (翻屏) fr an extra 30 minutes. The parent argued that nne f her friends’ parents let them have screens after 9 ’clck. “I thught, in this family, we dn’t cmpare urselves with ther peple, Dad?” the teenager replied. The parent — wh was me, by the way — just gt served. Since they were yung, I have tld my kids nt t cmpare themselves with ther peple. I have argued cuntless times that cmparisns are the “thief f jy”.
Althugh my daughter didn’t win, she did help expse ne f the wrst pieces f advice I have ever given. In my defence, I did what we’ve all dne befre, which is repeat received wisdm withut explring the nuances. But nw is the time t set the recrd straight, which starts with questining the idea that all scial cmparisn is unhealthy.
Scial cmparisns d, f curse, ften get us int emtinal truble. But they can be harnessed (利用) fr ur betterment if we understand hw they wrk. The scial cmparisns we make — nes that lead us t feel gd r bad abut urselves — are vital t ur ability t thrive (成长). Science prvides a guide we can use t harness the way we perfrm these cmparisns t reduce their negative emtinal impacts.
Cmparing yurself with smene wh is utperfrming yu culd result in feelings f envy if yu fcus n the things they have and yu dn’t, r it can be energizing and inspiring if yu use these cmparisns as a surce f mtivatin, fr example, “If they can achieve that, s can I.” Cmparing yurself with smene wh is ding wrse than yu culd result in fear and wrry if yu think abut hw yu culd fall int similar circumstances, r it can draw ut feelings f gratitude and appreciatin if yu use that cmparisn t braden yur views — fr example, “Ww, things culd be much wrse; I’m ding great.”
What I wish I taught my daughter earlier are these nuances. Hw we feel abut urselves rests nt just n whm we cmpare urselves with but als n hw we think abut that cmparisn. That’s smething we all have cntrl ver.
28. Hw did the authr feel abut his daughter’s argument?
A. Excited.B. Inspired. C. Energized.D. Relieved.
29. What des the wrd “nuances” underlined in Paragraph 2 mst prbably mean?
A. Majr achievements.B. Cmplex feelings.
C. Significant impacts.D. Fine differences.
30. Which wuld be the best title fr the passage?
A. Cmparing Ourselves with Others Can Becme a Healthy Habit
B. Cmparing Ourselves with Others Can Strengthen Family Ties
C. Scial Cmparisns Can Get Us int Emtinal Truble
D. Scial Cmparisns Can Be Cntrlled by Science
(2024·北京·高考)
The ntin that we live in smene else’s vide game is irresistible t many. Searching the term “simulatin hypthesis” (模拟假说) returns numerus results that debate whether the universe is a cmputer simulatin —— a cncept that sme scientists actually take seriusly. Unfrtunately, this is nt a scientific questin. We will prbably never knw whether it’s true. We can, instead, use this idea t advance scientific knwledge.
The 18th-century philspher Kant argued that the universe ultimately cnsists f things-in-themselves that are unknwable. While he held the ntin that bjective reality exists, he said ur mind plays a necessary rle in structuring and shaping ur perceptins. Mdern sciences have revealed that ur perceptual experience f the wrld is the result f many stages f prcessing by sensry systems and cgnitive (认知的) functins in the brain. N ne knws exactly what happens within this black bx. If empirical (实证的) experience fails t reveal reality, reasning wn’t reveal reality either since it relies n cncepts and wrds that are cntingent n ur scial, cultural and psychlgical histries. Again, a black bx.
S, if we accept that the universe is unknwable, we als accept we will never knw if we live in a cmputer simulatin. And then, we can shift ur inquiry frm “Is the universe a cmputer simulatin?” t “Can we mdel the universe as a cmputer simulatin? ” Mdelling reality is what we d. T facilitate ur cmprehensin f the wrld, we build mdels based n cnceptual metaphrs (隐喻) that are familiar t us. In Newtn’s era, we imagined the universe as a clck. In Einstein’s, we uncvered the standard mdel f particle (粒子) physics.
Nw that we are in the infrmatin age, we have new cncepts such as the cmputer, infrmatin prcessing, virtual reality, and simulatin. Unsurprisingly, these new cncepts inspire us t build new mdels f the universe. Mdels are nt the reality, hwever. There is n pint in arguing if the universe is a clck, a set f particles r an utput f cmputatin. All these mdels are tls t deal with the unknwn and t make discveries. And the mre tls we have, the mre effective and insightful we can becme.
It can be imagined that cmparable t the prcess f building previus scientific mdels, develping the “cmputer simulatin” metaphr-based mdel will als be a hugely rewarding exercise.
28. What des the authr intend t d by challenging a hypthesis?
A. Make an assumptin.B. Illustrate an argument.
C. Give a suggestin.D. Justify a cmparisn.
29. What des the phrase “cntingent n” underlined in Paragraph 2 prbably mean?
A. Accepted by.B. Determined by.C. Awakened by. D. Discvered by.
30. As fr Kant’s argument, the authr is _________.
A. appreciativeB. dubtful C. uncncerned D. disapprving
31. It is implied in this passage that we shuld _________.
A. cmpare the current mdels with the previus nes
B. cntinue explring the classical mdels in histry
C. stp arguing whether the universe is a simulatin
D. turn simulatins f the universe int realities up.
(2022·北京·高考)
Quantum ( 量子 ) cmputers have been n my mind a lt lately. A friend has been sending me articles n hw quantum cmputers might help slve sme f the biggest challenges we face as humans. I’ve als had exchanges with tw quantum-cmputing experts. One is cmputer scientist Chris Jhnsn wh I see as smene wh helps keep the field hnest. The ther is physicist Philip Taylr.
Fr decades, quantum cmputing has been little mre than a labratry curisity. Nw, big tech cmpanies have invested in quantum cmputing, as have many smaller nes. Accrding t Business Weekly, quantum machines culd help us “cure cancer, and even take steps t turn climate change in the ppsite directin.” This is the srt f hype ( 炒作 ) that annys Jhnsn. He wrries that researchers are making prmises they can’t keep. “What’s new,” Jhnsn wrte, “is that millins f dllars are nw ptentially available t quantum cmputing researchers.”
As quantum cmputing attracts mre attentin and funding, researchers may mislead investrs, jurnalists, the public and, wrst f all, themselves abut their wrk’s ptential. If researchers can’t keep their prmises, excitement might give way t dubt, disappintment and anger, Jhnsn warns. Lts f ther technlgies have gne thrugh stages f excitement. But smething abut quantum cmputing makes it especially prne t hype, Jhnsn suggests, perhaps because “‘quantum’ stands fr smething cl yu shuldn’t be able t understand.” And that brings me back t Taylr, wh suggested that I read his bk Q fr Quantum.
After I read the bk, Taylr patiently answered my questins abut it. He als answered my questins abut PyQuantum, the firm he c-funded in 2016. Taylr shares Jhnsn’s cncerns abut hype, but he says thse cncerns d nt apply t PyQuantum.
The cmpany, he says, is clser than any ther firm “by a very large margin ( 幅度 )” t building a “useful” quantum cmputer, ne that “slves an impactful prblem that we wuld nt have been able t slve therwise.” He adds, “Peple will naturally discunt my pinins, but I have spent a lt f time quantitatively cmparing what we are ding with thers.”
Culd PyQuantum really be leading all the cmpetitin “by a wide margin”, as Taylr claims? I dn’t knw. I’m certainly nt ging t advise my friend r anyne else t invest in quantum cmputers. But I trust Taylr, just as I trust Jhnsn.
31. Regarding Jhnsn’s cncerns, the authr feels ________.
A. sympatheticB. uncncernedC. dubtful D. excited
32. What leads t Taylr’s ptimism abut quantum cmputing?
A. His dminance in physics.B. The cmpetitin in the field.
C. His cnfidence in PyQuantum.D. The investment f tech cmpanies.
33. What des the underlined wrd “prne” in Paragraph 3 mst prbably mean?
A. Open.B. Cl.C. Useful. D. Resistant.
34. Which wuld be the best title fr the passage?
A. Is Jhnsn Mre Cmpetent Than Taylr?
B. Is Quantum Cmputing Redefining Technlgy?
C. Will Quantum Cmputers Ever Cme int Being?
D. Will Quantum Cmputing Ever Live Up t Its Hype?
(2021·北京·高考)
Early fifth-century philspher St．Augustine famusly wrte that he knew what time was unless smene asked him．Albert Einstein added anther wrinkle when he therized that time varies depending n where yu measure it．Tday's state-f-the-art atmic（原子的） clcks have prven Einstein right．Even advanced physics can't decisively tell us what time is, because the answer depends n the questin yu're asking．
Frget abut time as an abslute．What if，instead f cnsidering time in terms f astrnmy,we related time t eclgy？What if we allwed envirnmental cnditins t set the temp（节奏） f human life？We're increasingly aware f the fact that we can't cntrl Earth systems with engineering alne，and realizing that we need t mderate（调节）ur actins if we hpe t live in balance．What if ur definitin f time reflected that？
Recently，I cnceptualized a new apprach t timekeeping that's cnnected t circumstances n ur planet，cnditins that might change as a result f glbal warming．We're nw building a clck at the Anchrage Museum that reflects the ttal flw f several majr Alaskan rivers，which are sensitive t lcal and glbal envirnmental changes．We've prgrammed it t match an atmic clck if the waterways cntinue t flw at their present rate．If the rivers run faster in the future n average，the clck will get ahead f standard time．If they run slwer，yu'll see the ppsite effect．
The clck registers bth shrt-term irregularities and lng-term trends in river dynamics．It's a srt f bservatry that reveals hw the rivers are behaving frm their wn tempral frame（时间框架），and allws us t witness thse changes n ur smartwatches r phnes．Anyne wh pts t g n Alaska Mean River Time will live in harmny with the planet．Anyne wh cnsiders river time in relatin t atmic time will encunter a majr imbalance and may be mtivated t cunteract it by cnsuming less fuel r supprting greener plicies．
Even if this methd f timekeeping is nvel in its particulars，early agricultural scieties als cnnected time t natural phenmena．In pre-Classical Greece，fr instance，peple“crrected”fficial calendars by shifting dates frward r backward t reflect the change f seasn．Tempral cnnectin t the envirnment was vital t their survival．Likewise，river time and ther timekeeping systems we're develping may encurage envirnmental awareness．
When St．Augustine admitted his inability t define time， he highlighted ne f time 's mst nticeable qualities：Time becmes meaningful nly in a defined cntext．Any timekeeping system is valid，and each is as praisewrthy as its purpse．
31.What is the main idea f Paragraph 1？
A. Timekeeping is increasingly related t nature．
B. Everyne can define time n their wn terms．
C. The qualities f time vary with hw yu measure it．
D. Time is a majr cncern f philsphers and scientists．
32. The authr raises three questins in Paragraph 2 mainly t________．
A. present an assumptinB. evaluate an argument
C. highlight an experimentD. intrduce an apprach
33. What can we learn frm this passage？
A. Thse wh d nt g n river time will live an imbalanced life．
B. New ways f measuring time can help t cntrl Earth systems．
C. Atmic time will get ahead f river time if the rivers run slwer．
D. Mdern technlgy may help t shape the rivers’ tempral frame．
34. What can we infer frm this passage？
A. It is crucial t imprve the definitin f time．
B. A fixed frame will make time meaningless．
C. We shuld live in harmny with nature．
D. Histry is a mirrr reflecting reality．
（2020年北京卷）
Certain frms f AI are indeed becming ubiquitus. Fr example, algrithms (算法) carry ut huge vlumes f trading n ur financial markets, self-driving cars are appearing n city streets, and ur smartphnes are translating frm ne language int anther. These systems are smetimes faster and mre perceptive than we humans are. But s far that is nly true fr the specific tasks fr which the systems have been designed. That is smething that sme AI develpers are nw eager t change.
Sme f tday’s AI pineers want t mve n frm tday’s wrld f “weak” r “narrw” AI, t create “strng” r “full” AI, r what is ften called artificial general intelligence (AGI). In sme respects, tday’s pwerful cmputing machines already make ur brains lk weak. A GI culd, its advcates say, wrk fr us arund the clck, and drawing n all available data, culd suggest slutins t many prblems. DM, a cmpany fcused n the develpment f AGI, has an ambitin t “slve intelligence”. “If we’re successful,” their missin statement reads, “we believe this will be ne f the mst imprtant and widely beneficial scientific advances ever made.”
Since the early days f AI, imaginatin has utpaced what is pssible r even prbable. In 1965, an imaginative mathematician called Irving Gd predicted the eventual creatin f an “ultra-intelligent machine…that can far surpass all the intellectual (智力的) activities f any man, hwever clever.” Gd went n t suggest that “the first ultra-intelligent machine” culd be “the last inventin that man need ever make.”
Fears abut the appearance f bad, pwerful, man-made intelligent machines have been reinfrced (强化) by many wrks f fictin — Mary Shelley’s Frankenstein and the Terminatr film series, fr example. But if AI des eventually prve t be ur dwnfall, it is unlikely t be at the hands f human-shaped frms like these, with recgnisably human mtivatins such as aggressin (敌对行为). Instead, I agree with Oxfrd University philspher Nick Bstrm, wh believes that the heaviest risks frm A GI d nt cme frm a decisin t turn against mankind but rather frm a dgged pursuit f set bjectives at the expense f everything else.
The prmise and danger f true A GI are great. But all f tday’s excited discussin abut these pssibilities presuppses the fact that we will be able t build these systems. And, having spken t many f the wrld’s fremst AI researchers, I believe there is gd reasn t dubt that we will see A GI any time sn, if ever.
42. What des the underlined wrd “ubiquitus” in Paragraph I prbably mean?
A. Enrmus in quantity.B. Changeable daily.
C. Stable in quality.D. Present everywhere.
43. What culd AGI d fr us, accrding t its supprters?
A. Help t tackle prblems.B. Make brains mre active.
C. Benefit ambitius peple.D. Set up pwerful databases.
44. As fr Irving Gd’s pinin n ultra-intelligent machines the authr is ____________.
A. supprtiveB. disapprving
C. fearfulD. uncertain
45. What can be inferred abut AGI frm the passage?
A. It may be nly a dream.
B. It will cme int being sn.
C. It will be cntrlled by humans.
D. It may be mre dangerus than ever.
五年考情（2021-2025）
年份
词数
话题
考点分布
细节理解
推理判断
主旨大意
词义猜测
2025
406
对社会比较这一观念的思考：并非所有社会比较都不健康，可加以利用促进自我提升
0
1
1
1
2024
408
讨论科学问题：宇宙是否是由计算机模拟生成的
0
3
0
1
2022
400
量子计算真的会像它的宣传那样成功吗？
1
1
1
1
2021
480
文章通过讨论时间的定义，讲述了人们应该和大自然和谐相处，保护环境。
1
2
1
0
2020
433
工智能(AGI)实现的可能性
1
2
0
1
考情分析：
从内容上看，近年来北京高考议论文选材聚焦科技伦理、社会心理、哲学思辨等前沿话题，注重考查逻辑推理、观点辨析与作者态度。
从题型上看，以推理判断、词义猜测、主旨大意为主，选项干扰性强，需结合上下文精准把握论证逻辑与作者立场。
高频设问点多为标题、观点句、转折/让步处、举例/引用作用。难度为中等略高，要求快速定位论点并辨别论据与论证方法。
预计2026年北京高考议论文会出现。复习过程中要熟练掌握议论文的命题特点和解题方法，做到成功应对议论文阅读试题。
复习目标：
掌握议论文三要素（论点、论据、论证）的识别与分析能力。
强化推理判断技巧，能区分作者观点、他人观点及事实论据。
熟悉高频题型（如态度题、目的题、词义题）的解题路径。
提升对抽象概念、复杂句式的理解能力，快速抓取核心论点。
培养批判性思维，能评价论证有效性并归纳文章主旨。
题型1
段落大意题
每个段落都有一个中心思想,且中心思想通常会在段落的首句或尾句体现出来,这个句子就是常说的段落主题句。没有给出明显的主题句时,要根据段落内容概括出段落大意
题型2
文章大意题
考查考生把握全文主题和理解中心思想的能力,即考查考生的归纳概括能力
题型3
标题归纳题
概括出文章的中心思想,并对中心思想再次加以提炼,拟定出文章的标题。文章标题可以是单词、短语,也可以是句子
题型1
词义(词组)猜测题
考查考生猜测文章中某个生词或短语的含义,或考查该词能被哪个单词或短语代替
题型2
句意猜测题
考查考生准确理解作者的观点,尤其是对特定的人、物、事件的褒贬观点,以准确推断语句的含义
题型3
代词指代题
要求考生依据语境的逻辑关系
正确
选项
特征
逻辑通顺
内容上与词或短语所在的句子、段落有关，符合上下文逻辑；
代词查询
代词指代有时与上文距离较远，需要对前文内容进行总结才能得出结论；
本意延伸
真实的含义与其字面意思无关，在原意的基础上进行延伸或者拓展；
接近原意
采取转述、举例或列举等表达方式，与原句意思最接近。
干扰项特征
1.构词法干扰，指片面地从构词法的角度猜测、思考，不考虑上下文语境，望词生义；
2.句子解释中含有过多原句中已有的词和短语的选项一般是错误选项。
3拘泥于字面意思，根据所学过的熟词意义常使考生觉得画线词义背过而忽略上下文主观臆断。
张冠李戴
即把文章中作者的观点与其他人的观点混淆在一起。题干问的是作者的观点,选项中出现的却是其他人的观点;题干问的是其他人的观点,选项中却出现了作者的观点
无中生有
这种类型的干扰项往往是基本的生活常识或普遍认可的观点,但在文章中并无相关的信息支撑点。其次,这种干扰项也有可能与设置的问题毫不相干
曲解文意
即推测意义与文章表层意义有区别。推理判断题中有些选项来自文章中的某一句或某几句话,命题者可能会利用里面的词设计出干扰项,看似表达文章的意思,其实是借题发挥,是对原文意思的曲解
鱼目混珠
鱼目混珠类型的干扰项常出现在词句理解类试题的选项中,即利用某个词或句子的字面含义代替其在文章特定语境中的具体含义
扩缩范围
为了准确、严密地表达文章内容,命题者特别注意对文意范围的限定,有时通过加上almst, all, nearly, mre than, nrmally, usually等词语对文意范围加以限定。“扩缩范围”干扰法就是在选项中通过改变或去掉限制性词语,将信息的范围、程度、感情色彩等改变,从而给考生解题造成干扰的命题方法
偷梁换柱
干扰项用了与文章中某一句话相似的句型结构和单词,却在考生易忽视的地方换了几个单词,造成句意的改变
题型1
深层推断题
周密逻辑分析推断隐含意义
题型2
意图推断题
依据文体特点推断写作意图
题型3
文章出处题
根据文章体裁和内容推断文章出处
题型4
观点态度题
利用语境的褒贬性进行信息推断
题型5
人物性格特征题
利用感情色彩、态度观点的词汇推断
题型6
目标读者推断题
利用语言表达方式或者文章的内容推断
题型7
文章后续走势和篇章结构题
利用文章结构进行推断
1.bjective客观的
2.neutral中立的
3.factual事实的
4.disinterested无兴趣的
5.impartial公平的；不偏不倚的
6.impersnal无人情味的
7.unprejudiced无偏见的
8.unbiased无偏见的
9.uncncerned不关心的
10.detached独立的
1.enthusiastic热心的
2.supprtive支持的
3.ptimistic乐观的
4.psitive积极的
5.favrable有利的；赞成的
6.apprving赞成的
7.appreciative欣赏的
8.admiring钦佩的
9.impressive印象深刻的
1.suspicius可疑的
2.dubtful怀疑的
3.skeptical/sceptical怀疑的
4.questining质疑的
5.ppsed反对的
6.cntradictry自相矛盾的
7.negative消极的
8.disapprving不赞成的
9.critical批评的
10.disgusted反感的
11.irnic反话的；讽刺的
12.hstile敌对的
13.cntemptuus蔑视的
14.dismissive轻蔑的
15.pessimistic悲观的
16.glmy阴暗的；令人沮丧的
17.apprehensive忧虑的
1.mixed混合的
2.tlerant容忍的
3.indifferent漠不关心的
4.cncerned担心的
5.sensitive敏感的
6.reserved矜持的
7.cnservative保守的；守旧的
8.radical激进的
9.mderate中等的；适度的
10.mild温和的；温顺的
11.subjective主观的
12.incnclusive无定论的