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：研究人员产出评估对招聘委员会和资助机构至关重要，通常通过科学生产力、被引次数或h指数等综合指标衡量。由于年轻研究者需要时间积累被引次数和提升h指数，对他们的评估更为重要。因此，预测h指数有助于发现研究者的科学影响力。此外，识别影响科学影响力的关键因素，有助于研究者寻找改进方案。本研究考察了作者、论文及发表场所特定特征对未来h指数的影响。我们采用机器学习方法预测h指数，并通过特征分析技术深化对特征影响力的理解。利用Scopus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“基于既往影响力的特征”，包括发文量、被引次数及h指数；第二组为“非影响力特征”，包含作者、合著关系、论文及发表场所的特性。我们探究了这些特征对处于三个不同职业阶段研究者h指数预测的重要性，同时考察了不同特征类别预测性能的时间维度，以确定哪些特征适用于长期与短期预测。通过分析作者性别特征在预测任务中的作用，发现性别对h指数预测的影响极其微弱。研究结果表明，非影响力特征在短期预测中对年轻学者的预测效果优于资深学者；而长期预测中，基于既往影响力的特征预测能力下降程度超过其他特征。