The KANON-Index: A Computational Metric for Individual Scientific Contribution Based on Methodological Complexity, Citation Impact, and Authorship Structure

INTRODUCTION The evaluation of individual scientific contribution remains a major challenge in research assessment and academic training. Traditional bibliometric indicators, such as the h-index and Journal Impact Factor, rely heavily on citation counts and jornal prestige, neglecting key dimensions including authorship contribution, methodological complexity, and experimental investment. These limitations can distort how scientific work is valued and understood, particularly in educational contexts within Biology. Moreover, they reinforce simplified notions of productivity that may mislead students and early-career researchers regarding what constitutes high-quality science.  

Objectives

This study aims to develop and validate the KANON-Index (derived from the Greek word kanonicos), a multidimensional and automated bibliometric indicator designed to provide a more comprehensive assessment of individual scientific contribution, serving as a tool for classifying critical concepts in scientometrics.  

Methods

STRATEGY The KANON-Index integrates five normalized components: citation impact, authorship contribution, methodological complexity, experimental cost, and journal influence. Data were collected from OpenAlex, Crossref and ORCID databases, and methodological complexity was quantified using natural language processing with semantic deduplication of techniques. Validation was performed using a simulated dataset (N=100) and real-world data from 164 Nobel Prize laureates and 500 researchers across Medicine, Physics, Chemistry, and Economics.  

Results

The KANON-Index showed moderate correlation with traditional metrics (r=0.585–0.835), strong negative correlation with number of authors (r=-0.424 to -0.746), and positive correlation with methodological complexity (r=0.559–0.776). These findings indicate effective correction of hyperauthorship bias and recognition of experimental rigor. Cross-field analysis demonstrated adaptability through adjustable component weights, enabling fairer comparisons across disciplines. Additionally, the index identified high-impact researchers with low publication volume but high methodological contribution, highlighting its sensitivity to non-traditional excellence profiles.  

Conclusion

The KANON-Index provides a robust and scalable alternative to traditional metrics, capturing dimensions of scientific contribution often overlooked. In educational settings, it supports critical discussions on authorship, methodology, and research evaluation, contributing to more equitable and grounded ranking.