论文标题
大学排名的可靠性如何？
How Reliable are University Rankings?
论文作者
论文摘要
大学或大学排名几乎已成为自己的行业，由美国新闻\＆World Report（USNWR）和类似组织出版。大多数排名都使用类似的方案：排名降低的分数顺序，其中每个分数都是使用一组属性及其权重计算的；属性可以是客观的或主观的，而权重始终是主观的。该方案足够通用，可以应用于排名大学以外的其他对象。如相关工作所示，这些排名具有重要的含义和许多问题。在本文中，我们使用公立学院数据集对该排名计划进行了重新研究。我们既以多种方式正式和实验表明这种排名方案是不可靠的，并且不能被视为权威，因为它对重量变化太敏感，并且很容易被盖好。例如，我们展示了如何以编程方式得出合理的权重，以将数据集中的多个大学移至最高等级；此外，这项任务需要几秒钟的个人笔记本电脑上的600多个大学。我们的数学公式，方法和结果也适用于排名大学以外的其他对象。我们结论说，应将所有用于排名的数据和方法开放，以验证和可重复性。
University or college rankings have almost become an industry of their own, published by US News \& World Report (USNWR) and similar organizations. Most of the rankings use a similar scheme: Rank universities in decreasing score order, where each score is computed using a set of attributes and their weights; the attributes can be objective or subjective while the weights are always subjective. This scheme is general enough to be applied to ranking objects other than universities. As shown in the related work, these rankings have important implications and also many issues. In this paper, we take a fresh look at this ranking scheme using the public College dataset; we both formally and experimentally show in multiple ways that this ranking scheme is not reliable and cannot be trusted as authoritative because it is too sensitive to weight changes and can easily be gamed. For example, we show how to derive reasonable weights programmatically to move multiple universities in our dataset to the top rank; moreover, this task takes a few seconds for over 600 universities on a personal laptop. Our mathematical formulation, methods, and results are applicable to ranking objects other than universities too. We conclude by making the case that all the data and methods used for rankings should be made open for validation and repeatability.
