Revisiting the field normalization approaches/practices

2025-04-20

AI Summary

This paper addresses the persistent challenge in scientometrics of eliminating cross-disciplinary citation bias using field-normalization methods. It identifies a key limitation of source-side normalization (e.g., SNIP): its reduced efficacy stems from imbalanced growth rates of publications across disciplines. To overcome this, we propose a novel source–target co-normalization strategy that jointly integrates logarithmic transformation (ln(c+1)) and z-score standardization—marking the first formal unification of these two normalization pathways. Empirical evaluation on multidisciplinary datasets demonstrates that our method significantly outperforms the widely adopted ln(c+1)/μ baseline, improving cross-field metric consistency by 23% (p < 0.01). The approach advances fairness and robustness in interdisciplinary research assessment, offering both a conceptual paradigm shift and a reproducible technical framework for equitable bibliometric evaluation.

Technology Category

Natural Language Processing: Ethics — Bias, Fairness, Transparency & PrivacyApplication Domains: Humanities & Computational Social ScienceCognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

Social Networks and Social Media: Fairness and bias in social network and social media analysisSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsWeb Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

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.

Problem

• Evaluating effectiveness of field normalization methods in scientometrics
• Addressing citation bias across disciplines using logarithmic transformations
• Comparing source-side and target-side normalization for fair comparisons

Innovation

• Combines logarithmic and z-score normalization
• Addresses citation bias with dual normalization
• Evaluates source-side and target-side methods