Field normalization plays a crucial role in scientometrics to ensure fair comparisons across different disciplines. In this paper, we revisit the effectiveness of several widely used field normalization methods. Our findings indicate that source-side normalization (as employed in SNIP) does not fully eliminate citation bias across different fields and the imbalanced paper growth rates across fields are a key factor for this phenomenon. To address the issue of skewness, logarithmic transformation has been applied. Recently, a combination of logarithmic transformation and mean-based normalization, expressed as ln(c+1)/mu, has gained popularity. However, our analysis shows that this approach does not yield satisfactory results. Instead, we find that combining logarithmic transformation (ln(c+1)) with z-score normalization provides a better alternative. Furthermore, our study suggests that the better performance is achieved when combining both source-side and target-side field normalization methods.

翻译：领域归一化在科学计量学中对于确保不同学科间的公平比较起着至关重要的作用。本文重新审视了几种广泛使用的领域归一化方法的有效性。我们的研究结果表明，源端归一化（如SNIP所采用的方法）并不能完全消除不同领域间的引文偏差，而各领域论文增长率的不平衡是导致这一现象的关键因素。为了解决偏态问题，已应用了对数变换。最近，一种将对数变换与基于均值的归一化相结合的方法，表达为 ln(c+1)/μ，已变得流行。然而，我们的分析表明，这种方法并未产生令人满意的结果。相反，我们发现将对数变换（ln(c+1)）与z分数归一化相结合是一种更好的替代方案。此外，我们的研究表明，将源端与目标端领域归一化方法结合使用时，能获得更优的性能。