Persistence Paradox in Dynamic Science

TL;DR

This paper examines how persistence in scientific research can hinder adaptation during paradigm shifts, using the deep learning revolution as a case study to analyze scientists' career trajectories and impact.

Contribution

It introduces the concept of a rigidity penalty and demonstrates how strategic adaptation to emerging trends enhances scientific impact during paradigm shifts.

Findings

Scientists slow to adapt experience impact decline.

Strategic pivoting toward new trends yields higher impact.

Deep learning revolution reconfigured scientific influence.

Abstract

Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during periods of paradigm shift. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of over 5,000 scientists who were active in top machine learning venues during the preceding decade, we examine how their research focus and output evolved. We first uncover a dynamic period in which leading venues increasingly prioritized cutting-edge deep learning developments that displaced relatively traditional statistical learning methods. Scientists responded to these changes in markedly different ways. Those who were previously successful or affiliated with old teams adapted more slowly, experiencing what we term a rigidity penalty - a reluctance to embrace new directions leading to a decline in scientific impact, as measured by citation percentile rank. In contrast, scientists who pursued strategic adaptation - selectively pivoting toward emerging trends while preserving weak connections to prior expertise - reaped the greatest benefits. Taken together, our macro- and micro-level findings show that scientific breakthroughs act as mechanisms that reconfigure power structures within a field.