如何从g指数的任意性中获得优势
How to derive an advantage from the arbitrariness of the g-index
摘要：
g指数的定义和h指数一样随意，因为g篇被引用最多的论文的引用阈值g^2可以由一个前置因子来修改，从而考虑到数据集中或多或少的高引用出版物。在一个案例研究中，我调查了26个物理学家的引用记录，并表明前因子的影响排名的广义g-指数小于广义h-指数。我特别建议g指数的前因子为2，因为这样得到的值与普通h指数的值具有相同的数量级。通过这种方式，可以避免原始g指数的缺点，即值通常比h指数大得多，因此精度问题也大得多;同时保留了g指数相对于h指数的优点。与广义h指数一样，对于广义g指数，不同的前因子可能更适用于只关注引用频率高的顶级科学家或引用次数少的初级研究人员的研究。
英文摘要：
The definition of the g-index is as arbitrary as that of the h-index, because the threshold number g^2 of citations to the g most cited papers can be modified by a prefactor at one's discretion, thus taking into account more or less of the highly cited publications within a dataset. In a case study I investigate the citation records of 26 physicists and show that the prefactor influences the ranking in terms of the generalized g-index less than for the generalized h-index. I propose specifically a prefactor of 2 for the g-index, because then the resulting values are of the same order of magnitude as for the common h-index. In this way one can avoid the disadvantage of the original g-index, namely that the values are usually substantially larger than for the h-index and thus the precision problem is substantially larger; while the advantages of the g-index over the h-index are kept. Like for the generalized h-index, also for the generalized g-index different prefactors might be more useful for investigations which concentrate only on top scientists with high citation frequencies or on junior researchers with small numbers of citations.
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