Uncovering simultaneous breakthroughs with a robust measure of disruptiveness
Munjung Kim, Sadamori Kojaku, Yong-Yeol Ahn

TL;DR
This paper introduces a new neural embedding-based measure of disruptiveness that overcomes limitations of previous metrics, enabling better identification of disruptive innovations and simultaneous breakthroughs in science and technology.
Contribution
The authors develop a continuous, neural embedding framework for measuring disruptiveness, improving accuracy and enabling detection of simultaneous discoveries compared to the traditional disruption index.
Findings
The new measure better distinguishes disruptive works like Nobel papers.
It reveals simultaneous disruptions by identifying similar future contexts.
It provides a more robust and equitable evaluation of scientific breakthroughs.
Abstract
Progress in science and technology is punctuated by disruptive innovation and breakthroughs. Researchers have characterized these disruptions to explore the factors that spark such innovations and to assess their long-term trends. However, although understanding disruptive breakthroughs and their drivers hinges upon accurately quantifying disruptiveness, the core metric used in previous studies -- the disruption index -- remains insufficiently understood and tested. Here, after demonstrating the critical shortcomings of the disruption index, including its conflicting evaluations for simultaneous discoveries, we propose a new, continuous measure of disruptiveness based on a neural embedding framework that addresses these limitations. Our measure not only better distinguishes disruptive works, such as Nobel Prize-winning papers, from others, but also reveals simultaneous disruptions by…
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Taxonomy
TopicsInformation and Cyber Security · Software Engineering Research
