Exploring Citation Diversity in Scholarly Literature: An Entropy-Based Approach

2024-09-04

arXiv.org

AI Summary

Traditional citation counting overlooks structural distribution, failing to capture systemic imbalances in scholarly impact across dimensions such as nationality, discipline, institution, gender, and time. Method: We propose a novel citation diversity metric grounded in Shannon entropy, integrating entropy theory, Pareto distribution fitting, cross-layer statistical analysis, and century-scale temporal modeling. Contribution/Results: We find that highly cited works universally follow a variable-scale Pareto law; small and large economies exhibit clustering similarity in citation diversity; institutional count dynamics drive real-time reconfiguration of national groupings; and significant diversity disparities exist among Nobel laureates, across six major disciplines, and between male and female scientists. Our entropy-based measure sensitively captures structural inequities, offering a scalable, quantitative paradigm for scientometric evaluation and equity research.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Game Theory and Economic Paradigms: Fair Division
• Reasoning under Uncertainty: Causality

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

This study explores the citation diversity in scholarly literature, analyzing different patterns of citations observed within different countries and academic disciplines. We examine citation distributions across top institutions within certain countries and find that the higher end of the distribution follows a Power Law or Pareto Law pattern; the scaling exponent of the Pareto Law varies depending on the number of top institutions included in the analysis. By adopting a novel entropy-based diversity measure, our findings reveal that countries with both small and large economies tend to cluster similarly in terms of citation diversity. The composition of countries within each group changes as the number of top institutions considered in the analysis varies. Moreover, we analyze citation diversity among award-winning scientists across six scientific disciplines, finding significant variations. We also explore the evolution of citation diversity over the past century across multiple fields. A gender-based study in several disciplines confirms varying citation diversities among male and female scientists. Our innovative citation diversity measure stands out as a valuable tool for assessing the unevenness of citation distributions, providing deeper insights that go beyond what traditional citation counts alone can reveal. This comprehensive analysis enhances our understanding of global scientific contributions and fosters a more equitable view of academic achievements.

Problem

• Analyzing citation diversity patterns across countries and disciplines
• Measuring citation distribution unevenness using entropy-based approach
• Investigating gender differences in citation diversity among scientists

Innovation

• Entropy-based diversity measure for citations
• Power Law analysis of citation distributions
• Gender and discipline citation diversity study