Evaluation of researchers' output is vital for hiring committees and funding bodies, and it is usually measured via their scientific productivity, citations, or a combined metric such as h-index. Assessing young researchers is more critical because it takes a while to get citations and increment of h-index. Hence, predicting the h-index can help to discover the researchers' scientific impact. In addition, identifying the influential factors to predict the scientific impact is helpful for researchers seeking solutions to improve it. This study investigates the effect of author, paper and venue-specific features on the future h-index. For this purpose, we used machine learning methods to predict the h-index and feature analysis techniques to advance the understanding of feature impact. Utilizing the bibliometric data in Scopus, we defined and extracted two main groups of features. The first relates to prior scientific impact, and we name it 'prior impact-based features' and includes the number of publications, received citations, and h-index. The second group is 'non-impact-based features' and contains the features related to author, co-authorship, paper, and venue characteristics. We explored their importance in predicting h-index for researchers in three different career phases. Also, we examine the temporal dimension of predicting performance for different feature categories to find out which features are more reliable for long- and short-term prediction. We referred to the gender of the authors to examine the role of this author's characteristics in the prediction task. Our findings showed that gender has a very slight effect in predicting the h-index. We found that non-impact-based features are more robust predictors for younger scholars than seniors in the short term. Also, prior impact-based features lose their power to predict more than other features in the long-term.

翻译：研究人员产出的评估对招聘委员会和资助机构至关重要，通常通过其科学生产力、引文数量或如h指数等综合指标来衡量。评估年轻研究人员更为关键，因为获得引文和h指数的增长需要时间。因此，预测h指数有助于发现研究人员的科学影响力。此外，识别影响科学预测的关键因素，对于寻求提升自身影响力的研究人员具有指导意义。本研究探讨了作者、论文及期刊特定特征对未来h指数的影响。为此，我们采用机器学习方法预测h指数，并运用特征分析技术加深对特征影响的理解。利用Scopus中的文献计量数据，我们定义并提取了两组主要特征：第一组与既往科学影响力相关，称为“既往影响力特征”，包括论文数量、引文数和h指数；第二组为“非影响力特征”，涵盖作者、合著关系、论文及期刊特征。我们探究了这些特征在不同职业阶段研究人员h指数预测中的重要性，同时检验了不同特征类别预测性能的时间维度，以确定哪些特征在长期与短期预测中更为可靠。我们引入作者性别变量，以考察该作者特征在预测任务中的作用。研究发现，性别对h指数预测的影响极为微弱。此外，在短期预测中，非影响力特征对年轻学者的预测稳健性优于资深学者；而在长期预测中，既往影响力特征的预测效力较其他特征衰减更为显著。