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请问这句话什么意思哦Theresultscouldbetelling.“Ifthedisparitydropswithanonymization,that’scleare...
请问这句话什么意思哦The results could be telling. “If the disparity drops with anonymization, that’s clear evidence of bias,” says Nakamura.
原文如下One basic issue that the NIH will address is whether grant reviewers are thinking about an applicant’s race at all, even unconsciously. A team will strip names, racial identification and other identifying information from some proposals before reviewers see them, and look at what happens to grant scores. (Such identity stripping is surprisingly difficult: even citations might reveal who the applicant is, and reviewers need some information about an applicant to make a fair appraisal.) The results could be telling. “If the disparity drops with anonymization, that’s clear evidence of bias,” says Nakamura. 展开
原文如下One basic issue that the NIH will address is whether grant reviewers are thinking about an applicant’s race at all, even unconsciously. A team will strip names, racial identification and other identifying information from some proposals before reviewers see them, and look at what happens to grant scores. (Such identity stripping is surprisingly difficult: even citations might reveal who the applicant is, and reviewers need some information about an applicant to make a fair appraisal.) The results could be telling. “If the disparity drops with anonymization, that’s clear evidence of bias,” says Nakamura. 展开
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一个基本的问题,美国国立卫生研究院将地址是否给予评论关于申请人的种族在所有的思考,甚至不自觉地。一个团队将带钢的名字,在审稿人看到他们种族认同和其他身份信息的一些建议,看看会发生什么,给予评分。(这样的身份剥离是非常困难的:即使引用可能揭示那些申请人,人需要一些关于申请人作出公正的评价。信息)的结果可以告诉。“如果差距下降与匿名,这是偏见的明确证据,”中村邦夫说。
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这句话什么意思哦The results could be telling. “If the disparity drops with anonymization, that’s clear evidence of bias,” says Nakamura.
结合上下文,这句话的意思大致是:
(这个匿名测试的数据的)结果将能够判断(事实的真像)。“如果匿名情况下(不同种族入选者的人数)差距(显著)下降,这是很明显的种族歧视的证据,”Nakamura说。
结合上下文,这句话的意思大致是:
(这个匿名测试的数据的)结果将能够判断(事实的真像)。“如果匿名情况下(不同种族入选者的人数)差距(显著)下降,这是很明显的种族歧视的证据,”Nakamura说。
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有道翻译看看,我嫌麻烦。
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就是这句话里的drop with 怎么理解哦The results could be telling. “If the disparity drops with anonymization, that’s clear evidence of bias,” says Nakamura.
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