The Gerontocratization of Science: How hypergrowth reshapes knowledge circulation

TL;DR

The study investigates how exponential growth in scientific output shapes the circulation and renewal of knowledge, introducing the term gerontocratization to describe the increasing dominance and aging of the citation elites. Using OpenAlex-derived, discipline-spanning datasets and a minimal urn-based generative model, it demonstrates that hypergrowth correlates with rising inequality in citation shares, longer attention lifespans for a shrinking elite, and a slower discovery process. The authors show a universal Heaps' law for citations and provide evidence that growth alone can induce a stably aging canon, highlighting a fundamental tension between expansion and dynamism in science. These findings imply that policies and tools to counterbalance growth-driven stagnation are needed to maintain intellectual vitality and timely renewal of scientific canons.

Abstract

Scientific literature has been growing exponentially for decades, with publications from the last twenty years now comprising 60% of all academic output. While the impact of information overload on news and social-media consumption is well-documented, its consequences on scientific progress remain understudied. Here, we investigate how this rapid expansion affects the circulation and exploitation of scientific ideas. Unlike other cultural domains, science is experiencing a decline in the proportion of highly influential papers and a slower turnover in its canons. This results in the disproportionate persistence of established works, a phenomenon we term the ``gerontocratization of science''. To test whether hypergrowth drives this trend, we develop a generative citation model that incorporates random discovery, cumulative advantage, and exponential growth of the scientific literature. Our findings reveal that as scientific output expands exponentially, gerontocratization emerges and intensifies, reducing the influence of new research. Recognizing and understanding this mechanism is crucial for developing targeted strategies to sustain intellectual dynamism and ensure a balanced and healthy renewal of scientific knowledge.

Figures (8)

• Figure 1: (A) Relative number of publications by year for all the disciplines. (B) Average number of citations received by papers appeared in year $t$, after 2 years from their publication. (C) Fraction of papers, appeared in year $t$, without any citation after two years from their publications. Each discipline is characterized by a different color, while the square points represent the average measures among all datasets.
• Figure 2: (A) Gini index of the citation share $\xi_x^i(t)$ for each discipline on a year-by-year basis. (B) Evolution of the time $T_{peak}$ from the publication year to reach the peak with maximum share, averaged across papers published each year in each discipline. (C) Evolution of the cumulative fraction of share at the peak $f^C_{peak}$. (D) Evolution of the half-life time $T_{1/2}$ from the peak. Each discipline is characterized by a different color, while the square points represent the average measures among all datasets.
• Figure 3: (A) Ranked Jaccard similarity $J_{50}(t)$ between the top 50 papers in the citation ranking at year $Y$ and the ones at $Y-1$. (B) Ranked Jaccard similarity $J_{50}^{PR}(t)$of the top 50 papers according to Page Rank centrality in the citation network at $Y$ and those at $Y-1$s.
• Figure 4: (A) Relative size of the set of elite papers in time. (B) Average age of elite papers in time. (C) Temporal evolution of the relative co-citation density in the sub-graph of elite papers with respect to the whole co-citation network.
• Figure 5: (A) Heaps' law for the different disciplines, showing the evolution of the number of different cited papers as a function of the total number of citations. (B) Correlation between the exponent of the Heaps' law ($\beta$) and the growing exponent of the corpus size ($\alpha$).Each point is a discipline. The size of the points is proportional to the total size of the dataset. (C) Correlation between the realtive variation (between 1980 and 2015) of the Gini index and the growing exponent of the corpus size ($\alpha$). Each point is a discipline. The size of the points is proportional to the total size of the dataset.