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Erin Leahey

Publications and source records attributed to Erin Leahey.

7 recordsLinked to original sources
arXiv2026

The Longevity of Innovation

Modern science is organized around specialization in training and teamwork. Scientists develop deep expertise within a field and combine complementary knowledge through collaboration to solve complex problems. Yet whether specialization is the most effective path to sustained innovation remains unclear. Here we introduce a quantitative framework that distinguishes generalists from specialists based on scaling patterns of disciplinary mobility while remaining independent of career age and productivity. Applying this framework to 49 million publications produced by 3 million scientists between 1900 and 2020, we examine how research style relates to innovation, learning, collaboration, and productivity. We find that scientists who move across fields are more likely to sustain innovative contributions throughout their careers, whereas those who remain within narrow fields exhibit the age-related decline in innovation. Generalists are less anchored to the literature of their training. They are more likely to pursue research independently, and, when they collaborate, they preferentially partner with other generalists. Teams with a greater share of generalists produce more innovative research, even after accounting for differences in knowledge diversity. Despite these advantages, generalists publish fewer papers on average and have become less common over time. These findings reveal a tension between the longevity of scientific careers and the longevity of scientific innovation.

cs.SI↗
arXiv2026

Robust Evidence for Declining Disruptiveness: Assessing the Role of Zero-Backward-Citation Works

We respond to Holst et al.'s critique that the decline in scientific disruptiveness documented in Park et al. (Nature, 2023) is an artifact of including works with zero backward citations. Using their advocated dataset, metric, and exclusion criteria, we find declines equivalent to major benchmark transformations in science. Their own regression model--designed to address their concerns about zero-citation works--yields large and significant declines for both papers and patents (p<0.001), a result found in their supplementary tables yet left unaddressed, despite directly contradicting their central claim. Their critique is further undermined by severe quality issues in their data, which contain three times more zero-citation works than ours. We trace this excess to their inclusion of at least 2.8 million editorials, obituaries, and comments, 1.5 million books and proceedings, and 254,000 product and artistic reviews--in all, 20% of their sample is non-research content that almost by definition lacks backward citations. Simple keyword searches confirm the problem's severity, identifying among others 456 For Dummies guides, 50 Dr. Seuss and Curious George books, and the Captain Underpants series--all zero-citation entries in their sample. Applying granular document type classification to their data reveals that such non-research content fell from 40% to 8% of their sample between 1945 and 2010--a shift sufficient to generate the decline in zero-citation prevalence they attribute to metadata errors in our study. Standard practice excludes such content to guard against the metadata quality concerns at the center of their critique--concerns their dataset exemplifies rather than addresses. Declining disruptiveness has been documented in nearly 100 studies across multiple databases, metrics, and non-citation-based measures. The weight of evidence does not support an artifact-based explanation.

cs.SI↗
arXiv2026

Is Innovation Becoming Less Disruptive? An Inventory of the Literature

A growing literature has examined whether innovation is becoming less disruptive, spanning diverse domains and data sources and using a range of methodologies. This paper provides an inventory of 105 studies exploring this question. The evidence is largely consistent in direction. Studies spanning scientific papers, patents, products, legal cases, music, and visual art consistently report evidence of a decline. This pattern holds not only for citation-based measures, but also for text-based approaches, firm displacement rates, product similarity networks, and audio and visual embeddings. The literature has also identified notable exceptions, including rebounds in specific domains and predictable variation across field lifecycles. We catalog each study's data, methods, and findings to provide a resource for researchers and policymakers seeking to understand the current state of the evidence.

physics.soc-ph↗
arXiv2024

The Death of Renaissance Scientist

Scholars are often categorized into two types: hedgehogs (specialists), who focus on working within a specific research field, and foxes (generalists), who actively contribute to a variety of fields. Despite the familiar anecdotes and popularity of this distinction, its empirical foundation has remained largely unexamined. We examine whether the research style of being a fox or a hedgehog is a stable personal trait or an evolving strategy over a scientist's career. Analyzing 2.3 million scholars' publication records over a century, we find that research styles exhibit remarkable stability. Notably, the proportion of fox-like scientists has dramatically declined in the past century, a phenomenon we term "the death of Renaissance scientists." This decline is particularly significant as science shifts toward team collaboration. Teams of foxes consistently outperform teams of hedgehogs in generating new ideas and directions, as confirmed by two emerging innovation metrics for papers: atypicality and disruption. Our research is the first to quantify the process and consequences of the decline of Renaissance scientists. By doing so, we establish a universal link between research styles, demographic shifts, and innovative output.

cs.SI↗
arXiv2022

The decline of disruptive science and technology

Theories of scientific and technological change view discovery and invention as endogenous processes, wherein prior accumulated knowledge enables future progress by allowing researchers to, in Newton's words, "stand on the shoulders of giants". Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances. Yet contrary to this view, studies suggest that progress is slowing in several major fields of science and technology. Here, we analyze these claims at scale across 6 decades, using data on 45 million papers and 3.5 million patents from 6 large-scale datasets. We find that papers and patents are increasingly less likely to break with the past in ways that push science and technology in new directions, a pattern that holds universally across fields. Subsequently, we link this decline in disruptiveness to a narrowing in the use of prior knowledge, allowing us to reconcile the patterns we observe with the "shoulders of giants" view. We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices, or field-specific factors. Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of science and technology.

cs.SI↗
arXiv2022

Metrics and Mechanisms: Measuring the Unmeasurable in the Science of Science

What science does, what science could do, and how to make science work? If we want to know the answers to these questions, we need to be able to uncover the mechanisms of science, going beyond metrics that are easily collectible and quantifiable. In this perspective piece, we link metrics to mechanisms by demonstrating how emerging metrics of science not only offer complementaries to existing ones, but also shed light on the hidden structure and mechanisms of science. Based on fundamental properties of science, we classify existing theories and findings into: hot and cold science referring to attention shift between scientific fields, fast and slow science reflecting productivity of scientists and teams, soft and hard science revealing reproducibility of scientific research. We suggest that interest about mechanisms of science since Derek J. de Solla Price, Robert K. Merton, Eugene Garfield, and many others complement the zeitgeist in pursuing new, complex metrics without understanding the underlying processes. We propose that understanding and modeling the mechanisms of science condition effective development and application of metrics.

physics.soc-ph↗