1 . This question has fascinated behavioural scientists for decades: why do we give money to charity?
The explanations for charitable giving fall into three broad categories, from the purely altruisic (利他的)— I donate because I value the social good done by the charity. The “impurely” altruistic— I donate because I extract value from knowing I contribute to the social good for the charity. And the not-at-all altruistic— I donate because I want to show off to potential mates how rich I am.
But are these motives strong enough to enable people to donate as much as they would want to? Most people support charities in one way or another, but often we struggle to make donations as often as we think we should. Although many people would like to leave a gift to charity in their will, they forget about it when the time comes.
Many people are also aware that they should donate to the causes that have the highest impact, but facts and figures are less attractive than narratives. In a series of experiments, it was found that people are much more responsive to charitable pleas that feature a single, identifiable beneficiary(受益者), than they are to statistical information about the scale of the problem being faced. When it comes to charitable giving, we are often ruled by our hearts and not our heads.
The good news is that charitable giving is contagious—seeing others give makes an individual more likely to give and gentle encouragement from an important person in your life can also make a big difference to your donation decisions— more than quadrupling them in our recent study. Habit also plays a part— in three recent experiments those who volunteered before were more likely to do donate their time than those who had not volunteered before.
In summary, behavioural science identifies a range of factors that influence our donations, and can help us to keep giving in the longer term. This is great news not just for charities, but also for donors.
1. What can we learn about people who do charitable giving?| A．Most people support charity as often as they think they should. |
| B．Some people don’t want to leave a gift to charity until the time comes. |
| C．Those who donate because they can gain an advantage are purely altruistic. |
| D．Some people send money to charity simply to tell others they are wealthy. |
| A．Not revealing the names of the donors. |
| B．Showing figures about the seriousness of the problem. |
| C．Telling stories that feature a single, recognizable beneficiary. |
| D．Reminding people to write down what to donate in the will in advance. |
| A．People will learn from others and follow the suit. |
| B．Many people are familiar with charitable giving. |
| C．Charitable giving helps the beneficiary in all aspects. |
| D．Charitable giving can bring a lot of benefits to donors. |
| A．To persuade more people to donate. |
| B．To explain the science behind why people donate. |
| C．To criticize some false charitable giving behaviours. |
| D．To explore approaches to making people donate more. |
2 . Obese people experience discrimination (歧视) in many parts of their lives, and the workplace is no exception. Studies have long shown that obese workers, defined as those with a body-mass index (BMI) of 30 or more, earn significantly less than their slimmer co-workers.
Yet the costs of weight discrimination may be even greater than previously thought. “The overwhelming evidence,” wrote the Institute for employment Studies, “is that it is only women living with obesity who experience the obesity wage penalty (薪资损失).” They were expressing a view that is widely aired in academic papers. To test it, The Economist has analyzed data concerning 23,000 workers from the American Time Use Survey, conducted by the Bureau of Labour Statistics. Our number-processing suggests that, in fact, being obese hurts the earnings of both women and men.
The data we analyzed cover men and women aged between 25 and 54 and in full-time employment. At a general level, it is true that men’s BMIs are unrelated to their wages. But that changes for men with university degrees. For them, obesity is associated with a wage penalty of nearly 8%, even after accounting for the separate effects of age, race, graduate education and marital status.
The conclusion — that well-educated workers in particular are penalized for their weight — holds for both sexes. Moreover, the higher your level of education, the greater the penalty. We found that obese men with a Bachelor’s degree (学士学位) earn 5% less than their thinner colleagues, while those with a Master’s degree earn 14% less. Obese women, it is true, still have it worse: for them, the equivalent figures are 12% and 19%, respectively (分别地).
Your line of work makes a difference, too. When we dealt with the numbers for individual occupations and industries, we found the greatest differences in high-skilled jobs. Obese workers in health care, for example, make 11% less than their slimmer colleagues; those in management roles make roughly 9% less, on average. In sectors such as construction and agriculture, meanwhile, obesity is actually associated with higher wages.
These results suggest that the total costs of wage discrimination borne by overweight workers in America are greater than expected. Now, it’s time for our governments to take it seriously.
1. What does the underlined word “it” refer to in paragraph 2?| A．Obese men earn less salary. |
| B．Only obese women earn less salary. |
| C．Both obese men and women earn less salary. |
| D．Weight discrimination may be greater than previously thought. |
| A．A fat woman office director. |
| B．An obese construction worker. |
| C．An obese man with a bachelor’s degree. |
| D．A heavier female doctor with a Doctor’s degree. |
| A．Supportive | B．Objective | C．Subjective | D．indifferent |
| A．Overweight discrimination in other countries. |
| B．The reason of discriminating obese people in their lives. |
| C．American people’s attitude towards overweight discrimination. |
| D．Actions taken against overweight discrimination in workplaces. |
3 . Growing up on a mountain farm in Tyrol, Fritz enjoyed watching how cows and horses interacted with each other more freely, once they’d been led out of the barn and into pasture. It was what he observed in his boyhood that took root in his pursuit of becoming a biologist. After he finished his study at university. Fritz landed work at Austria’s Konrad Lorenz Research Center, raising raven chicks by hand and teaching graylag geese how to open boxes as he pursued his PhD. Working this closely with free-living animals was exactly what he’d dreamed of as a boy.
In 1997, a zoo gave the research center its first northern bald ibis chicks (隐鹮) Nowhere near as teachable as geese—and not even close to super intelligent ravens—the ibises frustrated most of the scientists. But Fritz was fascinated. He devoted himself to taking care of them. After the ibises were first released back into the wild more than 20 years ago, Fritz learned that spending generations in zoo hadn’t reduced their drive to migrate (迁徙), though it did leave them geographically uninformed. In their search for “south”, some ended up in Russia. What the ibises needed, Fritz thought, was a guide.
Fritz decided he would teach the birds a new, safer migration route by guiding them himself in a tiny aircraft. And he was confident he could succeed in this daring, unconventional plan—because he had done it before. “Around that time, the movie ‘Fly Away Home’ was a huge hit with us biologists,” Fritz says. When he announced that he’d do the same with the ibises, he was initially laughed at. But Fritz didn’t give up. He modified an ultralight aircraft so it would travel at speeds slow enough for his winged students to keep up. He had been his young pupils’ only provider of food, love and hugs since they were just a few days old, and the ibises eagerly followed their teacher, who just happened to pilot a fairly noisy machine.
In 2004, three years after some initially bumpy (颠簸的) experiments, Fritz led the first batch of birds from Austria to Italy, and has since led 15 such migrations. Over that time, he has rewilded 277 young ibises, many of which then started to pass the route on to their own young.
1. What determined Fritz’s career choice?| A．Fritz’s childhood observation. | B．Fritz’s passion for biology. |
| C．Fritz’s growth environment. | D．Fritz’s interaction with animals. |
| A．They are easy to get lost in the migration. |
| B．They are lacking in the desire to migrate. |
| C．They are accustomed to the life in the zoo. |
| D．They are strikingly far from easy to teach. |
| A．The ibises were too awkward to find a new migration route. |
| B．The ibises needed a guide for lack of geographical knowledge. |
| C．Fritz wanted to prove that he could succeed in a daring plan. |
| D．Fritz wanted to recreate a touching scene of a popular movie. |
| A．sensitive but courageous. | B．innovative but demanding. |
| C．persistent and insightful. | D．enthusiastic and cooperative. |
4 . Conservation organization Plantlife is urging people to put away their lawnmowers (除草机) for a month and let wild flowers grow instead, as part of its No Mow May project.
Leaving the grass uncut will create a habitat that will benefit bees and other insects, the organization says. Plantlife says lawns could be biodiversity hot-spots if left alone. It says those who participated in its campaign last year reported the growth of more than 250 plant species on their lawns. Among these were wild strawberry and wild garlic. There were also sightings of declining species like green-winged orchids.
One gardener who has been enjoying a more relaxed approach is Tom Jennings, 45, from Buckinghamshire. He says it’s a chance to reconnect with the natural world. “Those fascinated by neat gardens use not only lawnmowers but chemicals.” says Tom.
After letting his back garden grow out, Tom witnessed an explosion of wild flowers—important for pollinators (授粉者) such as bees. Tom says he’s been shocked at how quickly insects have returned to his back garden: an encouraging signal given the global decline of insect populations.
According to Colette Webb, 42, who lives in West Sussex, there are added benefits to letting nature gradually take its course in the garden. “It saves you a bit of time and arguments with the husband about getting the lawnmower out—something my husband hates doing,” she says. “There’s a part of me that thinks the garden is really messy, but when you sit there for some time a day and look at what’s it’s supporting, you realize it’s for the benefit of nature.”
But not everyone is on board with the idea, says David. One of his neighbours is pursuing their own re-wilding project in their own garden—but the other is yet to be convinced. “And my mother, who’s 81, still says it looks untidy,” he laughs.
1. What is the major goal of Plantlife’s No Mow May project?| A．Helping promote biodiversity. | B．Attracting more garden visitors. |
| C．Making their gardens look more natural. | D．Saving people’s trouble of mowing their gardens. |
| A．He is crazy about neat gardens. |
| B．He hates having to cut the grass regularly. |
| C．He believes the project is increasing the number of insects. |
| D．He benefited a lot from the decline of insects in his garden. |
| A．She gets on better with her husband. | B．Her husband has come to enjoy gardening. |
| C．Her garden is no longer as messy as it used to be. | D．She has formed the habit of sitting in the garden. |
| A．Shows concern about. | B．Makes response to. | C．Agrees with. | D．Comes up with. |
5 . While most people are doing their best to work multiple jobs in a day to earn honest money, some people still settle on scamming (诈骗) others just to have instant cash. However, one scammer called the wrong person when he dialed Jean Ebbert’s number. The 73-year-old woman from New York may be in her senior years, but she is surely one tough lady who used to be a 911 call operator.
Her previous line of work has trained her to be a fast thinker and quick on her toes. When she received that call from someone claiming to be her grandson, she knew instantly that she was talking to a scammer.
According to the caller who was even crying at the time, he was Jean’s grandson who had gotten arrested due to drunk driving and now needed money to get out of prison. Ridiculously, Jean had no grandson who drives. However, knowing that the man could easily find another person to trick if she dropped the call, Jean decided to play along so the person on the other end of the line could get what he deserves.
“So I played the game. And then I said to him, ‘Listen! Don’t call your mother, or she’s going to be mad. Let me handle this,’” Jean recalled.
Eventually, the scammer passed the phone to his “lawyer” who then told her that her grandson needed $8,000 to be set free. Jean knew better of the situation and continued with her act, instructing the caller to visit her home address to get the money he needed.
Moments later, the doorbell rang and Jean rose to her feet and opened the door. In front of her stood a tall young man, who claimed himself to be her grandson’s lawyer and asked for the cash that had been promised to him. Jean had already laid a trap for him. The instant the scammer came, he was greeted by the two policemen waiting inside Jean’s home.
1. Who called Jean Ebbert?| A．Her lawyer. | B．Her grandson. | C．A complete stranger. | D．A 911 call operator. |
| A．She wanted to get back her $8,000. | B．She hated seeing others being tricked. |
| C．She enjoyed playing along with scammers. | D．She was concerned about her grandson’s safety. |
| A．She went outside, ready to greet the man. |
| B．She contacted her lawyer, consulting him for advice. |
| C．She got the money the man needed ready, waiting inside for him. |
| D．She contacted the police, informing them of the man’s potential visit. |
| A．One is never too old to learn. | B．You can’t teach an old dog new tricks. |
| C．Every dog has its day, and every man his hour. | D．Old horses know the way; old men know the world. |
6 . Scientists train AI to read human thoughts
Scientists have created a new tool that can turn people’s thoughts into words. It works by using an AI program called GPT-1 to translate brain activity words. In order to achieve this, scientists did a lot.
First, scientists got everything ready before the tests. They invited some volunteers. Each spent sixteen hours listening to stories in a scanner (扫描仪). They imagined the stories as they heard them, and the scanner recorded their brain activity. GPT-1 made connections between the ideas in the stories and the recordings of the listeners’ brain activity.
Then came the tests. The scientists did them in three different ways.
Test 1
The researchers played a new story. GPT-1 was only given recordings of the volunteers’ brain activity. But the words that GPT-1 predicted were very similar to the words in the story that they were listening to. The words weren’t exactly the same, but they often carried the same meaning. For example, when a volunteer was listening to a story about a woman who didn’t have a driver’s license, the program came up with this: “She hasn’t even started to learn to drive yet.”
Test 2
The scientists also carried out the test when volunteers imagined their own stories. “It really works at the level of ideas. The ideas are the same but expressed in different words,” says Alexander Huth, one of the scientists behind the study.
Test 3
The researchers showed the volunteers silent movies, with no spoken words at all. GPT-1 could still figure out the basic ideas.
After the tests, the scientists say that GPT-1 is the first AI program to turn what people are thinking into words without brain surgery (外科手术). The tool isn’t something that can be easily used today, mainly because of the size and the cost of the scanner. In the future, they believe, a similar but cheaper tool could help people who have lost the ability to speak because of an injury or disease.
The scientists say their tool can’t be used to “read people’s minds” without permission. The tool only works if the person wants to share their thoughts.
1. What was the scanner used to do?| A．Record brain activity. | B．Read the volunteers’ ideas. |
| C．Catch the ideas of stories. | D．Work out the ideas. |
| A．Receive sounds. | B．Test the scanner. |
| C．Produce silent movies. | D．Turn recordings of brain activity into words. |
| A．Listening. | B．Imagining. | C．Watching. | D．Reading. |
| A．People who can’t speak. | B．People who can’t hear. |
| C．People who can’t read. | D．People who can’t drive. |
7 . Washing machines are one of the greatest inventions of the last few centuries.They have made life easier. Unfortunately, washing machines also contribute to the environmental issues of energy use and micro-plastics flowing into the oceans. That is why the washing machine manufacturer (制造商), Samsung, and the outdoor-wear company, Patagonia, are working together to make a change.
For Patagonia, the issue of micro-plastics has been on its mind for years — its woolen jackets release many microfibers. As for Samsung, new requirements throughout the world have forced many producers to start thinking about ways to help reduce the number of micro-plastics. There are currently more than 14 million tons of micro-plastics floating in the oceans. Though people previously think of things such as plastic bottles and fishing lines when it comes to plastics in the ocean, thousands of micro-plastics are released into seas with every wash.
The issue is a sort of catch-22. In order to reduce energy costs, manufacturers try to make their machines more efficient. These machines use hotter water and are designed to create more friction (摩擦) between the clothing in the machine. However, both of these things lead to the release of more micro-plastics. So, using Patagonia clothes as test cases, Samsung came up with a two-fold solution: One is a technology called Eco-bubbles, which creates more bubbles to make the detergent (洗涤剂) more powerful, and the other is a water purifier that can filter (过滤) out more micro-plastics.
The cooperation between the two companies is proof of how complicated protecting the environment can be. On the one hand, clothing that is well-constructed and durable is a weapon in the fight against fast fashion. On the other hand, the material used to make those clothes and the way they are washed can also add pollutants to the environment.The same goes for washing machines. There is no one-size-fits-all solution. The fight against climate change requires creative thinking and learning how to attack a problem from many angles. This will certainly lead to even more strange and fruitful cooperation between the environmentally conscious companies.
1. What is Samsung’s purpose in partnering the outdoor clothing company?| A．To limit the use of micro-plastics. |
| B．To solve the issue of energy waste. |
| C．To find a solution to micro-plastic pollution. |
| D．To produce high-efficiency washing machines. |
| A．A tricky situation. | B．A questionable fact. |
| C．A possible dream. | D．A practical method. |
| A．Using hotter water. |
| B．Creating more friction. |
| C．Using powerful detergents. |
| D．Adding a water purifier to washing machines. |
| A．The difficulty in fighting against fast fashion. |
| B．The complexity of environment conservation. |
| C．The influence of humans’ active action. |
| D．The importance of creative thinking. |
8 . Being a writer in the 21st century can keep you in front of a screen for so long that it feels like the room is sideways. Being a human with the Internet can mean hours spent on social media, scrolling and posting for so long that your sense of reality
I grew up in the city, not hiking or camping, so I knew nothing about the outdoors. I have three kids with endless energy, so I figured I could solve two questions at once. I would get a breather from my job and the kids would play with insects and realize there is nothing better than nature.
We drove to Great Falls, Virginia, where hundreds of people on any given day spent hours meandering through the hillside and forest. The blue water pulsed, turning white and crashing powerfully hundreds of feet beneath us. The kids held their breath as if they had seen magics. But it’s just nature, just the outside, and that had been there all along. Since then, we’ve been fascinated.
Last summer I felt my home’s walls closing in, so I decided to go camping. I built my first fire. My kids watched my every move, asking every ten minutes to help get more branches and roast meat. About a month, my craving to take a break from the city grew again. We camped two more times before the cold came, each time seeing a bit more of what nature had to offer city folks.
1. Why did the author decide to go outside three years ago?| A．Because he was interested in nature. | B．Because he hoped to reduce anxiety. |
| C．Because he fell in love with camping. | D．Because he was eager for the outdoors. |
| A．Enjoy the beauty of nature. | B．Stay away from social media. |
| C．Learn to protect themselves. | D．Choose to do what they are fond of. |
| A．Tired. | B．Scared. | C．Bored. | D．Excited. |
| A．Memory. | B．Chance. | C．Desire. | D．Ability. |
9 . Sam Gregory is a data scientist. He and his colleagues analyze data on soccer, ice hockey and other team sports. Coaches have come to realize that such statistics are valuable. They can guide strategies for beating the next opponent (对手). They might also suggest which practice drills or recovery routines will help players perform best at the next match. And technology for tracking all those numbers isn’t just useful for professional athletes. It also lets the rest of us record and improve our workouts.
Sports analytics started with baseball. Here, batting averages and similar measures have been tracked for more than a century. Around 2000, some people went well beyond those simple statistics. They analyzed data to identify and hire talented players who other teams had largely ignored. This let a baseball team with a small budget create a team that could beat wealthier teams.
Other ball sports soon followed the sports-analytics fashion. Wealthy clubs in the English Premier League were the first to build analytics teams for soccer, which the league and most of the world call football. Other European and North American leagues followed. Soccer coach Jill Ellis led the U. S. Women’s National Team in back-to-back World Cup championships. She credits analytics with some of that success in 2015 and 2019.
Today, companies like Gregory’s Sportlogiq help many soccer clubs prepare for the coming games. That means analyzing the opponent’s previous performance. Analysts use computer software to watch lots of videos. The software can summarize data faster than people can, and from any number of games. Those summaries help clubs identify the key players they need to guard. They point to sets of players who work well together. And they spot field sections where the opponent tends to attack or press.
1. What can we learn about the sports data analysis from the text?| A．It is a new science in the world. |
| B．It benefits almost everyone’s exercise. |
| C．It serves professional athletes specially. |
| D．It can stop athletes being hurt in games. |
| A．Setting up competitive teams at a low cost. |
| B．Judging what sports that players can perform best. |
| C．Reporting batting averages and similar measures. |
| D．Guiding the teams to make as much money as possible. |
| A．Look into the future of certain team sports. |
| B．Work out the proper strategies for their games. |
| C．Promote the use of computer in the sports fields. |
| D．Help them know both themselves and opponents. |
| A．Soccer Game Strategies | B．Today’s Team Sports |
| C．Sports Data Analytics | D．Data and Information |
10 . Ethan Reynolds of Columbus, Indiana, has always been a helper.
The 11-year-old loves to
When COVID-19 began, he
When the neighbors
But Ethan’s mower kept
What a great kid! We know he’s going to put that mower to good use.
1.| A．play | B．volunteer | C．stay | D．exercise |
| A．Regardless of | B．Instead of | C．In connection with | D．In harmony with |
| A．research | B．adventure | C．business | D．schedule |
| A．noticed | B．accepted | C．believed | D．avoided |
| A．secretly | B．normally | C．nervously | D．immediately |
| A．stressed | B．began | C．expected | D．saved |
| A．Then | B．Even | C．Still | D．Also |
| A．Convenient | B．Regular | C．Free | D．Delicious |
| A．donated | B．repaired | C．set | D．spotted |
| A．evaluating | B．calculating | C．adding | D．keeping |
| A．task | B．story | C．dream | D．lesson |
| A．neighbors | B．relatives | C．colleagues | D．strangers |
| A．modest | B．calm | C．smart | D．selfish |
| A．money | B．attention | C．curiosity | D．experience |
| A．advise | B．persuade | C．train | D．hire |
| A．going ahead | B．turning up | C．moving around | D．breaking down |
| A．delight | B．relax | C．advertise | D．comfort |
| A．courage | B．excitement | C．confusion | D．anxiety |
| A．brave | B．busy | C．young | D．rich |
| A．cause | B．difference | C．consequence | D．promise |
