Progress in science and technology is punctuated by disruptive innovation and breakthroughs. To understand disruptive innovations and their drivers, the ability to operationalize and estimate “disruptiveness” is critical. Yet, this task remains difficult because scientific influence propagates through both direct and indirect citation paths, and discoveries are often fragmented across multiple papers. Here, we introduce an embedding-based metric of disruptiveness. When applied to large-scale publication data, the measure not only reliably identifies canonical breakthroughs, such as Nobel Prize–winning papers, but also finds simultaneous disruptions that eluded standard approaches. By enabling more robust identification of disruptive innovations and simultaneous discoveries, our method facilitates more accurate attribution of transformative contributions while providing insights into the mechanisms driving scientific breakthroughs.
Kim et al. (Wed,) studied this question.