Anyone who has submitted a research paper knows the feeling. After months or years of work, the manuscript disappears into the peer review system. Days become weeks, weeks become months. You keep checking the submission portal, waiting for an answer. The final outcome certainly matters. Acceptance brings relief, rejection brings disappointment. But our study finds that the wait itself also matters, and more than we usually admit.
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Imagine a bustling city where a few towering skyscrapers dominate the skyline, while the surrounding neighborhoods—though full of life and potential—remain in the shadows, overlooked. This metaphor captures a troubling trend in modern science: a small group of prominent researchers increasingly dominate scholarly attention, while the majority of scientists—early-career scholars, specialists in niche fields, or those from underrepresented regions—struggle to be heard. A recent study, analyzing millions of academic publications and citations, reveals how this “attention inequality” is reshaping the scientific landscape, with profound implications for innovation, diversity, and the future of knowledge itself.
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In this episode of InfoFire, I sit down with Professor Mike Thelwall, a well accomplished scholar of Informetrics, to explore the intersections of Large Language Models (LLMs) and research evaluation. We delve into how LLMs are reshaping the landscape of research assessment, examining the promises they hold and the challenges they present in ensuring fair, meaningful, and context-aware evaluations.
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Information scientists adopt the references of a certain publication as a proxy for characterizing its interdisciplinarity.
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Over the past decade, there has been a growing public fascination with modern society’s complex “linkedness.”
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Simply put, prosopography investigates common background characteristics of a group of actors in history through a collective study of their lives.
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