My take was: 神秘的成才规律. 市面充斥成功人士成功学,但成功无法预测.各种考试考核也无预测成功人士.
有点天意: 寒门子弟就有机会! If you can duplicate your success, life repeats itself - your dad is billionaire, you're; your dad is Nobel, you're - nothing left for those without such dad. Some like dad, like son - that's ok. But if you can machinery duplicate - that's problematic.
"These papers add to a growing body of information suggesting that widely used “objective” admissions measures, such as GRE test scores and GPA, are exactly the wrong way to go about picking future contributors to scientific progress. Yet, they continue to strongly influence admissions committees—probably to the detriment of individual aspiring scientists who, despite their brilliance, may not look good on paper, and of the entire scientific enterprise."
"admissions committees often assume that “typical selection criteria [such as] standardized test scores, undergraduate GPA, letters of recommendation, a resume and/or personal statement highlighting relevant research or professional experience, and feedback from interviews with training faculty … correlate with research success in graduate school.”"
Yet, both the UNC and Vanderbilt studies found that none of the supposedly objective credentials predicted anything recognizable as scientific productivity—not first-author publications, conference presentations, fellowships or grants won, completing the Ph.D., passing the qualifying exam, or proceeding swiftly to dissertation defense or to the degree. Among the Vanderbilt sample, GRE scores turned out to be only “moderate predictors of first semester grades” and “weak to moderate predictors of graduate GPA,” the authors report. There is no convincing evidence of a “relationship between general GRE scores and graduate student success in biomedical research,” they write. At UNC, grades, amount of previous research experience (among students who all had at least some research experience), and faculty interview ratings all failed to foretell grad school productivity.
Posselt在筛选过程中特别是与精英研究生部门有关的另一个客观标准是申请人的本科学校的地位。 但是,旧金山加利福尼亚大学(UC)教授的2014年研究发现,这一指标也被淘汰出来,作为研究生表现的预测因子。 即使是美国新闻与世界报道“十大生命科学大学”之一的学士学位也没有明显差异。
Another supposedly objective criterion that Posselt found to be influential during the screening process, especially with elite graduate departments, is the standing of an applicant’s undergraduate school. But a 2014 study from a professor at the University of California (UC), San Francisco, found that this metric also washed out as a predictor of grad school performance. Even a bachelor’s degree from one of the U.S. News & World Report “top 10 life sciences universities” made no discernible difference.
那么数学家伊塘“汤姆”张是完全不为人知的,就同行评议的出版物像零和辅导教学中一样 - 在2013年,从接受博士学位的时候,他57岁,他提出了一个令人震惊数学世界的论文,解决了数学理论中长期存在的问题。现在被誉为“天才”和“名人”,此后,他获得了无数次大奖和两位教授的任命,首先是新罕布什尔大学,然后是加州圣巴巴拉分校。((Yitang "Tom" Zhang is a Chinese-born American mathematician working in the area of number theory. While working for the University of New Hampshire as a lecturer, Zhang ... After the Cultural Revolution ended, Zhang entered Peking University in 1978 as an undergraduate student and received his B.Sc. degree in ...))
If these widely used measures don’t work, what does? A group of researchers who devise and study metrics of research productivity and success wrote in 2012 that “the best way of predicting a scientist’s future success is for peers to evaluate scientific contributions and research depth.” They see the statistical method they developed as “useful” to “funding agencies, peer reviewers and hiring committees.” But even so, they make clear that, to ferret out that je ne sais quoi that foreshadows outstanding scientific performance, nothing compares to subjective judgments of quality by experienced researchers.
This emphasis on expert opinion also happens to align with the conclusions of the studies. The predictor that emerged as most powerful in both the UNC study and the UC San Francisco analysis was letters of recommendation from applicants’ undergraduate teachers—in other words, subjective assessments from people who presumably knew both them and their subjects well. Students who received top recommendations, the UNC co-authors suggest, show a “constellation of characteristics that typically correlate with research success [such as ability to] persevere and maintain focus and optimism in the face of regular challenges.”
And if objective measures such as scores and grades don’t work in predicting students’ scientific promise, can objective measures such as numbers of publications do any better at spotting true intellectual promise among faculty candidates? Not according to physicist Peter Higgs, whose work on subatomic particles in the 1960s inspired the long but ultimately successful hunt for the eponymous Higgs boson. As he told The Guardian in 2013, while traveling to Stockholm to receive the Nobel Prize in Physics, for years he had been “an embarrassment to [his] department when they did research assessment exercises.” With fewer than 10 papers published since this 1964 breakthrough, he often responded to departmental requests for lists of recent publications with a simple reply: “None.” Given today’s requirement to publish frequently, he added, “It's difficult to imagine how I would ever have enough peace and quiet in the present sort of climate to do what I did in 1964. … Today I wouldn't get an academic job. It's as simple as that. I don't think I would be regarded as productive enough.”
Then there’s mathematician Yitang “Tom” Zhang, who was completely unknown—as in zero peer-reviewed publications and an adjunct teaching job—when, in 2013, at the age of 57 and 12 years
out from receiving his Ph.D., he submitted a paper that astounded the mathematical world by
solving a long-standing problem in number theory. Now hailed as a “genius” and a “celebrity,” he has since
that triumph received numerous major prizes and appointments to two professorships, first at the University of New Hampshire and then UC Santa Barbara.
None of this is meant to suggest that every scanty publication list or so-so GRE score conceals hidden brilliance. But it does suggest a more reliable formula for spotting exceptional talent among people who appear not to possess it according to supposedly objective measures of scientific promise. It seems pretty likely that at least some of the people who knew and worked with Higgs and Zhang in their pre-fame days were aware of their abilities. It thus stands to reason that committees evaluating scientific potential, whether in grad school applicants or would-be faculty members, might benefit from paying more attention to what the scientists who know the candidates think of their minds and characters. Reading and considering such testimony would undoubtedly take more time and effort and could feel less “scientific” than looking at numbers, whether test scores, GPAs, or tallies of publications. But it appears more likely to pay off.