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Billions at Stake: How Self-Citation Adjusted Metrics Can Transform Equitable Research Funding

Published: April 25, 2025 | arXiv ID: 2504.20081v3

By: Rahul Vishwakarma, Sinchan Banerjee

Potential Business Impact:

Makes science awards fairer by fixing fake citations.

Business Areas:
Quantified Self Biotechnology, Data and Analytics

Citation metrics serve as the cornerstone of scholarly impact evaluation despite their well-documented vulnerability to inflation through self-citation practices. This paper introduces the Self-Citation Adjusted Index (SCAI), a sophisticated metric designed to recalibrate citation counts by accounting for discipline-specific self-citation patterns. Through comprehensive analysis of 5,000 researcher profiles across diverse disciplines, we demonstrate that excessive self-citation inflates traditional metrics by 10-20%, potentially misdirecting billions in research funding. Recent studies confirm that self-citation patterns exhibit significant gender disparities, with men self-citing up to 70% more frequently than women, exacerbating existing inequalities in academic recognition. Our open-source implementation provides comprehensive tools for calculating SCAI and related metrics, offering a more equitable assessment of research impact that reduces the gender citation gap by approximately 8.5%. This work contributes to the paradigm shift toward transparent, nuanced, and equitable research evaluation methodologies in academia, with direct implications for funding allocation decisions that collectively amount to over $100 billion annually in the United States alone.

Page Count
8 pages

Category
Computer Science:
Digital Libraries