A widely used measure of scientific impact is citations. However, due to
their heavy-tailed distribution, citations are fundamentally difficult to
predict. Instead, to characterize scientific impact, we address two analogous
questions asked by many scientific researchers: "How will my h-index evolve
over time, and which of my previously or newly published papers will contribute
to it?" To answer these questions, we perform two related tasks. First, we
develop a model to predict authors' future h-indices based on their current
scientific impact. Second, we examine the factors that drive papers---either
previously or newly published---to increase their authors' predicted future
h-indices. By leveraging relevant factors, we can predict an author's h-index
in five years with an R2 value of 0.92 and whether a previously (newly)
published paper will contribute to this future h-index with an F1 score of 0.99
(0.77). We find that topical authority and publication venue are crucial to
these effective predictions, while topic popularity is surprisingly
inconsequential. Further, we develop an online tool that allows users to
generate informed h-index predictions. Our work demonstrates the predictability
of scientific impact, and can help scholars to effectively leverage their
position of "standing on the shoulders of giants."

本文介绍了一种基于当前科研影响力的预测作者未来 h 指数的模型，并分析了导致文章对未来 h 指数贡献的因素。研究发现，学术权威和出版场所对预测科研影响力至关重要。

科学影响力能够被预测吗？

Can Scientific Impact Be Predicted?

Scientific impact plays a central role in the evaluation of the output of
scholars, departments, and institutions. A widely used measure of scientific
impact is citations, with a growing body of literature focused on predicting
the number of citations obtained by any given publication. The effectiveness of
such predictions, however, is fundamentally limited by the power-law
distribution of citations, whereby publications with few citations are
extremely common and publications with many citations are relatively rare.
Given this limitation, in this work we instead address a related question asked
by many academic researchers in the course of writing a paper, namely: "Will
this paper increase my h-index?" Using a real academic dataset with over 1.7
million authors, 2 million papers, and 8 million citation relationships from
the premier online academic service ArnetMiner, we formalize a novel scientific
impact prediction problem to examine several factors that can drive a paper to
increase the primary author's h-index. We find that the researcher's authority
on the publication topic and the venue in which the paper is published are
crucial factors to the increase of the primary author's h-index, while the
topic popularity and the co-authors' h-indices are of surprisingly little
relevance. By leveraging relevant factors, we find a greater than 87.5%
potential predictability for whether a paper will contribute to an author's
h-index within five years. As a further experiment, we generate a
self-prediction for this paper, estimating that there is a 76% probability that
it will contribute to the h-index of the co-author with the highest current
h-index in five years. We conclude that our findings on the quantification of
scientific impact can help researchers to expand their influence and more
effectively leverage their position of "standing on the shoulders of giants."

该论文研究了科学影响力的衡量指标，使用真实学术数据集分析研究者的学术影响力指标 h-index 的提高因素，指出研究者在该主题领域的权威性以及论文发表的期刊是提高 h-index 的关键因素。

这篇论文会增加您的 h 指数吗？科学影响力预测

Will This Paper Increase Your h-index? Scientific Impact Prediction

The h-index is a popular bibliometric indicator for assessing individual
scientists. We criticize the h-index from a theoretical point of view. We argue
that for the purpose of measuring the overall scientific impact of a scientist
(or some other unit of analysis) the h-index behaves in a counterintuitive way.
In certain cases, the mechanism used by the h-index to aggregate publication
and citation statistics into a single number leads to inconsistencies in the
way in which scientists are ranked. Our conclusion is that the h-index cannot
be considered an appropriate indicator of a scientist's overall scientific
impact. Based on recent theoretical insights, we discuss what kind of
indicators can be used as an alternative to the h-index. We pay special
attention to the highly cited publications indicator. This indicator has a lot
in common with the h-index, but unlike the h-index it does not produce
inconsistent rankings.

本文从理论角度批判了 h 指数在衡量科学家总体科学影响力时表现出的反直觉特性，并主张高被引用文献指数可以作为一个比 h 指数更适当的替代性指标，因为前者不存在不一致排名的问题。

h 指数的不一致性

The inconsistency of the h-index

The number h of papers with at least h citations has been proposed to
evaluate individual's scientific research production. This index is robust in
several ways but yet strongly dependent on the research field. We propose a
complementary index hI = h^2/N_t, with N_t being the total number of authors in
the considered h papers. A researcher with index hI has hI papers with at least
hI citation if he/she had published alone. We have obtained the rank plots of h
and hI for four Brazilian scientific communities. Contrasting to the h-index
curve, the hI index present a perfect data collapse into a unique allowing
comparison among different research areas.

该研究论文提出了一种名为 hI 指数的科学研究生产力评估指标，它可以消除学科领域的影响，与传统 h 指数相比，具有更强的数据对比能力。