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TOEIC・English Undergraduate

このプリントの穴埋めをして英文和英しなさいという問題です。助けてください

英語2A レポート課題(2026年前期) 以下の英文中の( 内に入れるのに適切と思われる1語を、 下の 入れなさい。 そのうえで全文を和訳しなさい。 の中から選んで ite of national diger Most funny stories are based on comic situations. In spite of national differences, certain funny situations have a ( 1 ) appeal. No matter ( 2 ) you live, you would find (3) difficult not to laugh at, say, Charlie Chaplin's early films. However, a new type of humor, called 'sick humor', has come into fashion. The following example of 'sick humor' will enable you to judge for yourself. A man ( 4 ) had broken his right leg was taken to a hospital a few days before Christmas. From the moment he arrived there, he kept on annoying his doctor to tell him ( 5 ) he would be able to go home. He felt afraid ( 6 ) having to spend Christmas in the hospital. On Christmas Day, the man still had his right leg in plaster. He spent a miserable day in bed thinking of all the ( 7 ) he was missing. The following day, however, the doctor consoled him by telling that his chances of being able to leave the hospital ( 8 ) time for New Year Celebrations were ( 9 ). The man took heart and, sure enough, on New Year's Eve he managed to walk along to a party. To ( 10 ) for his unpleasant experiences in the hospital, the man drank a little more than was good for him. He was still grumbling about hospitals at the end of the party when he slipped on a piece of ice and broke his left leg. blame compensate money yourself where of in at by with fun good whose who it when special universal

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TOEIC・English Undergraduate

この長文問題の答えと解説をお願いします。

15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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Economics Undergraduate

ピグー効果という例外を除いたとして、 単純に「貨幣市場が流動性のわなに陥っている場合には、物価の下落によって実質貨幣供給量が増加してもそれが国民所得の増加をもたらさないので、総需要曲線は垂直となる。」理由を教えてほしいです。

Ⅰ 【問題22-2】 国民所得と物価水準の関係を表す総需要曲線と総供給曲線に関する次の記 述のうち, 最も妥当なのはどれか。 1. 政府支出の増加は, IS曲線の右上方へのシフトを通じて総需要曲線を右 Movie 145 上方へシフトさせるが, 総需要の増加に対応して生産が拡大するので総供給曲線を右 下方へシフトさせることになる。 ! 2. 貨幣市場が流動性のわなに陥っている場合には、 ピグー効果が働かないとすれば 物 価の下落によって実質貨幣供給量が増加してもそれが国民所得の増加をもたらさない ので,総需要曲線は垂直となる。 ! 3. 総供給曲線の傾きは投資の利子弾力性の大きさによって決定され, 利子弾力性がゼロ! の場合には,総供給曲線は垂直になり, 弾力性が無限大の場合には水平となる。 4. 貨幣供給量の増加は、物価の上昇を通じて総供給曲線を左上方にシフトさせるだけで なく,利子率の低下を通じて投資を増加させるので,総需要曲線を右上方へとシフト させる。 5. 貨幣賃金が上昇する場合には,労働供給量の増加により生産が拡大するので,総供給 曲線は右下方にシフトするが, 賃金上昇が消費需要を拡大させるので、総需要曲線は 右上方にシフトすることになる。 (国家Ⅱ種)

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