Scientific prizes and the extraordinary growth of scientific topics

The extraordinary growth of scientific topics, characterized by unexpectedly large increases in impact and size, has been a subject of interest. While classical studies focused on individual cases, the availability of large-scale data allows for statistical analysis. This study explores the connection between scientific prizes and the extraordinary growth of topics. Scientific prizes, once limited to specific disciplines, have proliferated, impacting the careers of prizewinners through recognition and changing perceptions of their work. However, the impact of prizes on the growth of entire scientific topics remains uncertain. Some argue that increased interest in a prizewinner's work translates to increased interest in the related topic, while others suggest that prizes signal the topic's maturity, leading to decreased interest. This study investigates the statistical association between prizes and the growth of a topic's productivity, impact, and scientist migration.

Existing literature examines how awards impact prizewinners' careers, showing that pre-award publications receive accelerated citations post-award, and that winning a prize increases the likelihood of future wins. However, whether this effect extends to topic-level growth is unclear. Theoretical arguments are divided; some propose that prizes boost topic interest, while others argue the opposite, suggesting prizes might signal a topic's completion, leading to a decline in interest. This study aims to bridge this gap by examining the statistical relationship between prizes and abnormal growth in topic-level metrics.

The study used data on 405 scientific prizes (conferred 2900 times between 1970 and 2007) and over 10,000 scientific topics from 19 disciplines. Prize data was collected from Wikipedia and validated using dedicated webpages and print media. Topics were linked to prizes by associating prizewinners with their 'known-for' topics, operationally defined as topics on which they published 10 or more papers. This was cross-validated using Wikipedia's 'Known-For' dataset. Scientific topic data was sourced from the Microsoft Academic Graph (MAG), which uses crowdsourcing, AI, and NLP to classify publications into topics. To test for a statistical relationship between prize-winning and extraordinary growth, a difference-in-differences (DID) regression design with Dynamic Optimal Matching (DOM) was employed. DOM identified five non-prize-winning topics for each prize-winning topic, matched on six growth criteria (productivity, citations, impact of leading scientists, number of incumbent scientists, number of new entrants, and number of star scientists) for 10 years before the prize year. The analysis compared post-prize growth of prize-winning topics to the matched control group, measuring the difference in growth (Δt). Additional analyses examined the relationship between prize characteristics (money, discipline-specificity, and research recency) and the magnitude of extraordinary growth (Δ10), controlling for various factors such as funding and visibility of the prize.

The results strongly indicate a positive association between prize-winning and extraordinary growth. Compared to matched topics, prize-winning topics exhibited significant increases in all six growth measures. Table 1 presents DID regression results showing that while there were no significant differences before the prize year, prize-winning topics showed significantly greater growth post-prize (all p-values < 0.001). Figure 2 visually displays this extraordinary growth, showing a consistent increase in growth measures for prize-winning topics relative to matched topics in the years following the prize. Five years after the prize, the growth gap ranged from 17% to 30%, increasing to 25%–55% at 10 years. Specifically, prize-winning topics were 39.8% more productive, had 32.6% more citations, and experienced a 25% greater increase in the citation impact of leading scientists. These topics also retained 54.8% more incumbent scientists, gained 36.7% more new entrants (46.3% being rookie scientists), and attracted 47% more star scientists. Further analysis showed a positive correlation between extraordinary growth and paradigmatic diversification. Importantly, funding did not explain the extraordinary growth; prize-winning topics did not have significantly more funding before or after the prize compared to matched topics (Figure 3). Analysis at the individual topic level confirmed the generalizability of the findings, with 60% of prize-winning topics showing greater growth than their matched counterparts. Finally, analysis revealed that prize characteristics such as recency, discipline-specificity, and (to a lesser extent) prize money positively predicted the magnitude of extraordinary growth (Figure 4).

The study's findings address the research question by demonstrating a robust link between scientific prizes and the extraordinary growth of associated research topics. The significant increases in productivity, impact, and scientist attraction for prize-winning topics highlight the substantial influence of these awards. The results are relevant to the field because they offer a large-scale, empirical investigation into the dynamics of scientific innovation and the role of recognition mechanisms in shaping research trajectories. The finding that prize characteristics, rather than funding, primarily drive this growth challenges existing assumptions and provides valuable insights into science policy and resource allocation. The association between extraordinary growth and paradigmatic diversification suggests that prizes might play a crucial role in fostering new research directions and potential paradigm shifts.

This study demonstrates a strong and consistent relationship between scientific prizes and the extraordinary growth of scientific topics. Prize characteristics, particularly recency and discipline-specificity, are key predictors of the magnitude of this growth. Surprisingly, funding does not appear to be a significant driver. Future research should explore the underlying mechanisms driving this phenomenon and investigate potential implications for science policy and funding strategies. Further research could also explore how prize committees could improve the equity and representation of awardees.

The study relies on publicly available data, which might introduce biases. The reliance on Wikipedia and MAG for data collection might present limitations in terms of data completeness and potential biases in topic classification. Also, the analysis focuses primarily on NIH funding data, and the findings may not generalize to all types of research funding or funding practices. While the study establishes a correlation between prizes and extraordinary growth, it cannot definitively establish causality. Future studies using experimental or quasi-experimental designs could strengthen causal inferences.