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    Whether in the workplace or on the football field, effective teamwork can produce amazing results. However, working successfully as a team is not as easy as it may seem. Effective teamwork certainly does not just happen automatically; it takes a great deal of hard work and compromise. There are a number of factors that must be in place to make a good team.

    Effective leadership is one of the most important factors of good teamwork. The team's leader should possess the skills to create and preserve a positive working environment and motivate and inspire the team members to take a positive approach to work and be highly committed. An effective team leader will promote a high level of spirit and make them feel supported and valued.

    Communication is a vital factor of all interpersonal relationship and especially that of a team. Team members must be able to articulate their feelings, express plans and goals, share ideas and see each other's opinions.

    Conflicts will arise no matter how well a team functions together. The best way to deal with conflicts is to have some organized methods of handling conflicts. Team members should be able to voice their concerns without fear of offending others. Instead of avoiding conflict issues, a hands-on approach that settles them quickly is much better. It is often advised that the team leader sit with the conflicting parties and help work out their differences without taking sides and trying to remain objective if possible.

    The team leader must set a good example to create good teamwork. In order to keep team members positive and committed and motivated, the team leader himself needs to show these qualities. The team turns to the leader for support and guidance. So any negative words or behaviors on the leader's part can be disastrous.

    Regardless of what type of work you are in, knowing how to effectively work on and with a team is going to be extremely important to your success and that of your team.

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    Advances in artificial intelligence and the use of big data are changing the way many large companies recruit (招募) entry level and junior management positions. These days, graduates' CVs may well have to impress an algorithm (算法) rather than an (human resources) manager.

    While algorithms supposedly treat each application equally, experts are divided about whether so-called robo-recruitment promises an end to human prejudice in the selection process —or whether it may in fact add to it.

    “AI systems are not all equal,” says Loren Larsen, chief technology officer for HireVue, which has developed an automated video interview analysis system. “I think you have to look at the science team behind the work,” says Mr Larsen.

    The problem, experts say, is that to find the best candidates an algorithm has first to be told what “good” looks like in any given organization. Even if it is not given criteria that seem discriminatory, a powerful machine-learning system will quickly be able to copy the characteristics of existing workers. If an organization has favoured white male graduates from well-known universities, the algorithm will learn to select more of the same.

    The growing dependence on automation to judge suitability for everything from a loan to a job worries Yuriy Brun, an associate professor specializing in software engineering. “It takes a lot of the time for a company to put out software but it doesn't know if it is discriminatory” he says. Prof Brun explains that, considering the use of big data, algorithms will unavoidably learn to discriminate.

    Many of those working with robo-recruiters are more optimistic. Kate Glazebrook, the leader and co-founder of Applied, a hiring platform, says her task is to encourage hiring manager to move away from such indicators of quality as schools or universities and move to more evidence-based methods. When candidates complete tests online, Applied hides their names and shows the tests the candidates have completed, question by question, to human assessors. Every stage of the process has been designed to remove prejudice.

    With the same aim, Unilever decided in 2016 to switch to a more automated process for its graduate-level entry programme. Unilever worked with HireVue, Amberjack, which provides and advises on automated recruitment processes, and Pymetrics, another high volume recruitment company, which developed a game-based test in which candidates are scored on their ability to take risks and learn from mistakes, as well as on emotional intelligence. Unilever says the process has increased the ethnic diversity of its listed candidates and has been more successful at selecting candidates who will eventually be hired.

    “The things that we can do right now are impressive, but not as impressive as we're going to be able to do next year or the year after,” says Mr Larsen.

    Still, robo-recruiters must be regularly tested in case prejudice has occurred without anyone realizing it, says Frida Polli, the leader and co-founder of Pymetrics. “The majority of algorithmic tools are most likely causing prejudice to continue existing. The good ones should be examined.”