同行评议期刊投稿与接收论文分布的季节性熵、多样性和不平等性测度
Seasonal Entropy, Diversity and Inequality Measures of Submitted and Accepted Papers Distributions In Peer-Reviewed Journals
摘要：
本文提出了一种新的方法来发现特征的变量分布源于时间序列的分析。我们将该方法应用于同行评审期刊中提交和接受的论文。我们提供了一个比较研究的编辑决定提交给两个同行评审期刊的论文：塞尔维亚化学学会杂志（JSCS）和MDPI熵杂志。我们涵盖了近三年来提交的论文，约600份文件，以JSCS和2500熵的命运，是完全确定的。而不是比较这些文件的数量分布作为时间的函数相对于一个均匀的分布，我们分析相关的概率，从中我们得到的信息熵。有人认为，这种可能性确实比提交的实际数量对提交人更有意义。我们将这种熵分析与所谓的变量分布的多样性联系起来。此外，我们强调熵和多样性之间的对应关系与不等式的措施，如赫芬达尔-赫希曼指数和泰尔指数，本身是在类的熵措施;基尼系数，也衡量排序的多样性计算作进一步讨论。在这个例子中，同行评审过程的季节性方面进行了概述。据发现，使用这样的指数，非线性变换的数据分布，允许区分功能和演变的同行评审过程中作为时间的函数，以及比较非均匀性的分布。此外，t-和z-统计检验用于测量结果的显著性（p-水平），即如果论文在几个特定的月份或“季节”提交，则它们是否更有可能被接受;可预测性强度取决于期刊。
英文摘要：
This paper presents a novel method for finding features in the analysis of variable distributions stemming from time series. We apply the methodology to the case of submitted and accepted papers in peer-reviewed journals. We provide a comparative study of editorial decisions for papers submitted to two peer-reviewed journals: the Journal of the Serbian Chemical Society (JSCS) and this MDPI Entropy journal. We cover three recent years for which the fate of submitted papers, about 600 papers to JSCS and 2500 to Entropy, is completely determined. Instead of comparing the number distributions of these papers as a function of time with respect to a uniform distribution, we analyze the relevant probabilities, from which we derive the information entropy. It is argued that such probabilities are indeed more relevant for authors than the actual number of submissions. We tie this entropy analysis to the so called diversity of the variable distributions. Furthermore, we emphasize the correspondence between the entropy and the diversity with inequality measures, like the Herfindahl-Hirschman index and the Theil index, itself being in the class of entropy measures; the Gini coefficient which also measures the diversity in ranking is calculated for further discussion. In this sample, the seasonal aspects of the peer review process are outlined. It is found that the use of such indices, non linear transformations of the data distributions, allow to distinguish features and evolutions of peer review process as a function of time as well as comparing non-uniformity of distributions. Furthermore, t- and z- statistical tests are applied in order to measure the significance (p-level) of the findings, i.e. whether papers are more likely to be accepted if they are submitted during a few specific months or "season"; the predictability strength depends on the journal.
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