In modern academia, your worth can often be reduced to a few digits — an h-index, an impact factor, or a spot on the ‘Stanford Top 2%’ list. Metrics originally meant to simplify evaluation have turned into career-defining hierarchies that decide who gets hired, promoted, or funded. But are these numbers really measuring what matters? Or are they quietly distorting the purpose of science itself?
The Mirage of Rankings
Every year, social media lights up with proud announcements of inclusion in the Stanford Top 2 % Scientists ranking. Compiled from Scopus citation data, this list was never an official Stanford University endorsement — yet it has come to symbolize elite scientific status.
The problem is that the list is purely algorithmic. It reflects how often a researcher’s papers are cited, not the quality, originality, or societal value of their work. It ignores who actually did the heavy lifting in a collaboration and treats every author alike.
The Myth of the “Top 2 %” List
• Purely citation-based, not peer-reviewed.
• Ignores field differences and author contribution.
• Inflated by self-citations and mass co-authorships.
• Not an official evaluation by Stanford University.
Such lists flourish because academia has grown addicted to easy metrics — a culture where numbers substitute for nuance.
The Perils of Impact-Factor-Based Careers
If citation rankings are one illusion, Impact Factor obsession is another — and perhaps more damaging. Originally developed to help librarians choose journals, the IF is now widely misused to judge individual researchers. In India and elsewhere, recruitment, promotion, and even the selection of institutional heads are often based on IF-weighted scores — a practice both unscientific and unjust.
What this culture creates
Quantity over quality: Researchers chase frequent publications rather than meaningful breakthroughs.
Paper-mill temptations: The pressure to publish in “high-impact” journals fuels unethical authorship and predatory publishing.
Bias against local relevance: Hundreds of credible Indian and regional journals, published by universities, societies, and research institutions, do not carry an official Impact Factor despite maintaining rigorous peer review. Work on region-specific problems — climate resilience, indigenous crops, local biodiversity, traditional medicine — rarely finds space in the “indexed” global journals that count for promotion. As a result, such journals remain undervalued, and scientists who publish in them are penalized for serving local needs.
Misplaced leadership: When the same IF-based scoring systems are used to appoint directors, deans, and research heads, leadership itself becomes skewed. The system rewards those who mastered journal strategy — not those with vision, mentorship, or institution-building capability.
The result: departments led by number-rich scientists rather than idea-rich ones.
As the Leiden Manifesto (Nature, 2015) warned, “Journal-based metrics must not be used to assess individual researchers.” Yet across institutions, Impact Factor still reigns — distorting incentives, discouraging innovation, and suffocating local scholarship.
Why Traditional Metrics Miss the Point
Over-reliance on simplistic metrics like total citations and the h-index harms scientific integrity. These flawed measures reward longevity over originality, disregard mentorship and societal benefit, and even count retracted papers. This approach favors easily citable work over groundbreaking research, stifling creativity. A holistic evaluation is crucial for a scientific culture valuing innovation, mentorship, and societal impact over mere numbers.
The rapidly evolving landscape of artificial intelligence necessitates the development of novel tools for accurately assessing the impact and reach of research. Traditional metrics and evaluation methods, while still valuable, may not fully capture the nuances and widespread influence of studies conducted within or influenced by the AI era. Fortunately, we are beginning to see the emergence of innovative new tools specifically designed to address this challenge. These emerging assessment frameworks aim to provide a more comprehensive and insightful understanding of how research, particularly in AI, is shaping various fields, industries, and society at large.
GScholarLens: Measuring Contribution, Not Just Count
A promising shift is emerging from the Indian Institute of Technology Hyderabad, where Gaurav Sharma and team have built GScholarLens — an open-access browser extension that integrates with Google Scholar to compute an authorship-normalized index called the Scholar h-index (Sh-index).
Instead of crediting every co-author equally, GScholarLens weights citations according to authorship role:
Corresponding author – 100 %
First author – 90 %
Second author – 50 %
Co-authors – 25 % (≤ 6 authors) or 10 %(≥ 7 authors)
It also flags retracted papers (via Retraction Watch), visualizes citations by authorship and journal quartile, and charts year-wise trends. The Sh-index is not a final answer, but it marks a step toward fair, contribution-aware evaluation that recognizes intellectual leadership over nominal authorship.
Funding the Frontier: Following the Ripple Effect of Research
While GScholarLens focuses on who contributed, Funding the Frontier (FtF), developed by Dashun Wang at Northwestern University, tracks what research actually influences. By integrating data from Dimensions, Altmetric, Overton, and SciSciNet, FtF links grants, papers, patents, policy documents, and clinical trials — over 7 million grants and 140 million publications.
Its machine-learning algorithms can forecast which projects are most likely to yield tangible societal benefits, from patents to policy impact. In Alzheimer’s research, for example, FtF found that while past breakthroughs centered on biological mechanisms, future impact may lie in social-support studies — a perspective invisible to traditional metrics. Though critics warn against algorithmic bias, FtF’s strength lies in making the invisible visible: tracing the downstream influence of science on society.
Toward Responsible and Transparent Research Assessment
Together, GScholarLens and FtF reflect a new philosophy:
GScholarLens restores fairness in individual credit: It fairly attributes individual academic credit by moving beyond traditional, biased metrics. It uses a comprehensive methodology, incorporating qualitative factors and collaborative contributions to accurately assess an individual’s impact, especially in interdisciplinary projects.
FtF restores meaning in measuring societal outcomes: FtF (Framework for Thoughtful Futures) restores integrity to measuring societal outcomes. It advocates a multi-dimensional approach beyond narrow economic indicators, encompassing social well-being, environment, culture, and equitable access. This robust framework provides a deeper understanding of long-term impact for better policy decisions.
Complementary Tools: Both GScholarLens and FtF enhance human judgment, not replace it. They provide analytical frameworks and data-driven insights to inform decision-making. Human expertise, intuition, and ethical considerations remain crucial for interpretation, contextualization, and application of these insights, making judgment more informed and impactful.
Advancing Fair and Accountable Research Evaluation
Research evaluation is evolving to prioritize individual fairness and societal accountability. This requires a multi-faceted approach combining quantitative and qualitative assessments.
Authorship-Weighted Metrics for Individual Fairness: Develop and adopt metrics that quantify each author’s intellectual contribution in multi-author publications, ensuring fair credit beyond author order. This considers factors like conceptualization, methodology, data analysis, and writing.
Societal-Impact Analytics for Accountability: Evaluate research’s broader societal impact, moving beyond citations to assess real-world problem-solving, policy influence, public health improvements, or economic development. This ensures investments translate into meaningful benefits, tracking indicators like media mentions, policy briefs, and patent applications.
Structured Author-Contribution Data (CRediT Taxonomy) in Article Metadata: Systematically include CRediT (Contributor Roles Taxonomy) in article metadata to provide transparent, standardized, and machine-readable data on individual contributions. This enhances fairness, discoverability of expertise, and nuanced team assessments. Examples include “conceptualization,” “data curation,” and “writing – original draft.”
Peer Review and Narrative CVs for Qualitative Context: Complement quantitative metrics with qualitative insights. Expand peer review to include individual contributions and broader impact. Utilize narrative CVs to allow researchers to detail their impact, leadership, and career trajectory, providing a more comprehensive and equitable assessment than metrics alone.
For India and other emerging research systems, reforming evaluation norms away from IF-linked scoring is urgent. A country’s scientific leadership cannot be built on journal rankings; it must rest on scientific purpose.
Reclaiming the Meaning of “Impact”
When promotions and leadership appointments depend on impact factors and citation scores, academia produces papers, not progress. The true impact of science is not how often it is cited, but how deeply it transforms understanding, policy, and people’s lives.
The next decade should belong to ethical, data-driven, contribution-aware evaluation — one that values collaboration, originality, and relevance. Tools like GScholarLens and Funding the Frontier show that it’s possible to reclaim the real spirit of research assessment: measuring contribution instead of counting visibility.
If we must measure science, let us at least measure what truly matters.
Additional Reading
Hicks, D., Wouters, P., Waltman, L. et al. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature 520, 429–431 (2015). https://doi.org/10.1038/520429a
Karthik, V., Anand, I. S., Mahanta, U., & Sharma, G. (2025). Authorship-Contribution Normalized Sh-Index and Citations Are Better Research Output Indicators. arXiv preprint ://arxiv.org/abs/2509.04124
Moher, D., Naudet, F., Cristea, I.A., Miedema, F., Ioannidis, J.P.A., & Goodman, S.N. (2018). Assessing scientists for hiring, promotion, and tenure. PLoS Biol. 16(3):e2004089. https://doi.org/10.1371/journal.pbio.2004089.
Owens, B. (2025). Will Your Study Change the World? This AI Tool Predicts the Impact of Your Research. Nature, 630(8017), 611–613. https://doi.org/10.1038/d41586-025-03120-6
Pan, R. K., & Fortunato, S. (2014). Author Impact Factor: Tracking the Dynamics of Individual Scientific Impact. Scientific Reports 4(4880). https://doi.org/10.1038/srep04880
Hello Dr. Santhosh, your points are valid and strong. Keep going