シャドーイング練習: Cambridge Ielts 16 Reading test 1 Passage 3 - 動画で英語スピーキングを学ぶ

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3. The Future of Work According to a leading business consultancy,

3-14% of the global workforce will need to switch to a different occupation within the next 10-15 years,

and all workers will need to adapt as their occupations evolve alongside increasingly capable machines.

Automation, or Embodied Artificial Intelligence, AI, is one aspect of the disruptive effects of technology on the labor market.

Disembodied L, like the algorithms running in our smartphones, is another.

Dr. Stella Pacitti from Cambridge Judge Business School believes

that some of the most fundamental changes are happening as a result of the algorithmication of jobs

that are dependent on data rather than on production, the so-called knowledge economy.

Algorithms are capable of learning from data to undertake tasks that previously needed human judgment, such as reading legal contracts,

analyzing medical scans, and gathering market intelligence.

In many cases, they can outperform humans, says Pachidi.

Organizations are attracted to using algorithms because they want to make choices based on what they consider as perfect information, as well as to reduce costs and enhance productivity.

But these enhancements are not without consequences, says Pachidi.

If routine cognitive tasks are taken over by AI, how do professions develop their future experts?

She asks.

One way of learning about a job is legitimate peripheral participation.

A novice stands next to experts and learns by observation.

If this isn't happening, then you need to find new ways to learn.

Another issue is the extent to which the technology influences or even controls the workforce.

For over two years, Puccini monitored a telecommunications company.

The way telecom salespeople work is through personal and frequent contact with clients, using the benefit of experience to assess a situation and reach a decision.

However, the company had started using an algorithm that defined

when account managers should contact certain customers about which kinds of campaigns and what to offer them.

The algorithm, usually built by external designers, often becomes the keeper of knowledge, she explains.

In cases like this, Pachitti believes,

a short-sighted view begins to creep into working practices whereby workers learn through the algorithm's eyes and become dependent on its instructions.

Alternative explorations, where experimentation and human instinct lead to progress and new ideas, are effectively discouraged.

Pachidi and colleagues even observed people developing strategies to make the algorithm work to their own advantage.

We are seeing cases where workers feed the algorithm with false

data to reach their targets she reports it's scenarios like these

that many researchers are working to avoid their objective is to make ai technologies more trustworthy

and transparent so that organizations

and individuals understand how ai decisions are made in the meantime

says pachidi We need to make sure we fully understand the dilemmas that this new world raises regarding expertise, occupational boundaries, and control.

Economist Professor Hamish Lowe believes that the future of work will involve major transitions across the whole life course for everyone.

The traditional trajectory of full-time education followed by full-time work followed by a pension retirement is a thing of the past, says Lowe.

Instead, he envisages a multi-stage employment life, one where retraining happens across the life course,

and where multiple jobs and no job happen by choice at different stages.

On the subject of job losses, Mo believes the predictions are founded on a fallacy, it assumes that the number of jobs is fixed.

If in 30 years, half of 100 jobs are being carried out by robots,

that doesn't mean we are left with just 50 jobs for The number of jobs will increase.

We would expect there to be 150 jobs.

Dr. Ewan Mitkauhi at Cambridge's Centre for Business Research in King's College London agrees

that apocalyptic views about the future of work are misguided.

It's the laws that restrict the supply of capital to the job market, not the advent of new technologies that causes unemployment.

His recently published research answers the question of whether automation, AI and robotics will mean a jobless future by looking at the causes of unemployment.

it.

History is clear that change can mean redundancies, but social policies can tackle this through retraining and redeployment.

He adds, if there is going to be changed to jobs as a result of AI

and robotics then I'd like to see governments seizing the opportunity to improve policy to enforce good job security.

We can reprogram the law to prepare for a fairer future of work and leisure.

McGauhe's findings are a call to arms to leaders of organizations, governments and banks to preempt for coming changes with bold new policies that guarantee full employment,

fair incomes and a thriving economic democracy.

The promises of these new technologies are astounding.

They deliver humankind the capacity to live in a way that nobody could have once imagined humans.

Just as the Industrial Revolution brought people past subsistence agriculture and the corporate revolution enabled mass production,

a third revolution has been pronounced.

But it will not only be one of technology.

The next revolution will be social.

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このレッスンの語彙とスピーキングのポイント

このC1レベルのスピーキングレッスンは、動画「Cambridge Ielts 16 Reading test 1 Passage 3」を教材にしています。繰り返し出てくる語は次のとおりです：algorithm, pachidi, believes, revolution, technologies。この動画には、シャドーイング用の文が59文、単語が839語あります。音声の長さは5:47です。話す速さは1分あたり約145語で安定しており、シャドーイングしやすいペースです。英語の頻出3,000語に含まれる単語は77%だけなので、語彙は難しめです。

この動画の重要語彙

動画の中で特に難しい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
algorithm 名詞	/ˈælɡəɹɪðm̩/	アルゴリズム, 演算手順
robotics 名詞	/ɹoʊˈbɑ.tɪks/	ロボット工学, ロボティックス
automation 名詞	/ˌɔ.təˈmeɪ.ʃən/	自動化, オートメーション
workforce 名詞	/ˈwɝk.foɹs/	労働力, 労働人口
astound 動詞	/əˈstaʊnd/	驚かす
fallacy 名詞	/ˈfæl.ə.si/	いんちき, 欺瞞
redundancy 名詞	/ɹɪˈdʌndən(t)si/	冗長, 冗長性
salesperson 名詞		店員, 売り手
telecommunication 名詞	/ˌtɛləkəˌmjunɪˈkeɪʃən/	電気通信
trustworthy 形容詞	/ˈtɹʌst.wɜː.ði/	頼もしい, 信頼できる
discourage 動詞	/dɪsˈkɝɪd͡ʒ/	落胆させる, がっかりさせる
trajectory 名詞	/tɹəˈd͡ʒɛktəɹi/	軌道, 弾道
undertake 動詞	/ˌʌndəˈteɪk/	取り掛かる
thrive 動詞	/θɹajv/	繁栄する
dilemma 名詞	/daɪˈlɛmə/	ジレンマ

動画に出てくる句動詞

単語	発音	意味
carry out 動詞	/ˈkæ.ɹi aʊt/	行う, 実行する
take over 動詞		引き受ける

注意したい発音

話し手はdoesn't, I'd, isn'tなど、短縮形や弱形を3回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: algorithm /ˈælɡəɹɪðm̩/, trustworthy /ˈtɹʌst.wɜː.ði/, thrive /θɹajv/
「sh」と「zh」の音: automation /ˌɔ.təˈmeɪ.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/, telecommunication /ˌtɛləkəˌmjunɪˈkeɪʃən/, occupational /ɑkjuˈpeɪʃənəl/, leisure /ˈlɛʒə(ɹ)/
長い単語（アクセントの位置に注意）: automation /ˌɔ.təˈmeɪ.ʃən/, apocalyptic /əˈpɒ.kə.lɪp.tɪk/, consultancy /kənˈsʌl.tən.si/, disembodied /dɪsɪmˈbɒdiːd/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: algorithm /ˈælɡəɹɪðm̩/, peripheral /pəˈɹɪf(ə)ɹ(ə)l/, leisure /ˈlɛʒə(ɹ)/
/v/ — /b/ にならないように、上の歯を下唇に当てる: disruptive /dɪsˈɹʌptɪv/, envisage /ɛnˈvɪzɪd͡ʒ/, novice /ˈnɑvɪs/, advent /ˈæd.vɛnt/, thrive /θɹajv/
/f/ — 「フ」ではなく、上の歯と下唇で出す: workforce /ˈwɝk.foɹs/, fallacy /ˈfæl.ə.si/, outperform /ˌaʊt.pɚˈfɔɹm/, peripheral /pəˈɹɪf(ə)ɹ(ə)l/, enforce /ɪnˈfoɹs/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
通常の速度で一文ずつシャドーイングし、話し手のリズムに合うまで繰り返します。
自分の声を録音して元の音声と比べます。algorithm, robotics, automationなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Cambridge Ielts 16 Reading test 1 Passage 3 - 動画で英語スピーキングを学ぶ

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