arXiv subjects

Russell J. Funk

Publications and source records attributed to Russell J. Funk.

10 recordsLinked to original sources

arXiv2026

Large Language Models Explain Experts Better Than Experts Themselves

Tacit knowledge, or the "know-how" embedded in experience, is difficult to articulate, making its transfer a challenge in organizations. Tacit knowledge is hard to externalize (transform into explicit knowledge), and expertise is often poorly documented and lost when experts leave. This study examines whether LLMs can externalize tacit knowledge from experts' behaviors and whether such externalized knowledge supports downstream decision-making and transfer to novices. Across two studies, we show that LLM-externalized tacit knowledge improves decision quality and enables novices to approach expert-level performance, often outperforming knowledge articulated by human experts. These findings provide empirical support for Polanyi's Paradox -- that we can know more than we can tell -- and highlight the potential of LLMs as scalable tools that can help overcome human experts' articulation bottleneck. Mechanism analyses and robustness checks show that LLMs meaningfully learn and extract knowledge from expert conversations, and findings generalize across models and retrieval methods.

cs.HC↗

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↗

arXiv2025

Opening Knowledge Gaps Drives Scientific Progress

Knowledge production is often viewed as an endogenous process in which discovery arises through the recombination of existing theories, findings, and concepts. Yet given the vast space of potential recombinations, not all are equally valuable, and identifying those that may prove most generative remains challenging. We argue that a crucial form of recombination occurs when linking concepts creates knowledge gaps-empty regions in the conceptual landscape that focus scientific attention on proximal, unexplored connections and signal promising directions for future research. Using computational topology, we develop a method to systematically identify knowledge gaps in science at scale. Applying this approach to millions of articles from Microsoft Academic Graph (n = 34,363,623) over a 120-year period (1900-2020), we uncover papers that create topological gaps in concept networks, tracking how these gap-opening works reshape the scientific knowledge landscape. Our results indicate that gap-opening papers are more likely to rank among the most highly cited works (top 1-20%) compared with papers that do not introduce novel concept pairings. In contrast, papers that introduce novel combinations without opening gaps are not more likely to rank in the top 1% for citation counts, and are even less likely than baseline papers to appear in the top 5% to 20%. Our findings also suggest that gap-opening papers are more disruptive, highlighting their generative role in stimulating new directions for scientific inquiry.

cs.CY↗

arXiv2025

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↗

arXiv2024

Scientific and technological knowledge grows linearly over time

The past few centuries have witnessed a dramatic growth in scientific and technological knowledge. However, the nature of that growth - whether exponential or otherwise - remains controversial, perhaps partly due to the lack of quantitative characterizations. We evaluated knowledge as a collective thinking structure, using citation networks as a representation, by examining extensive datasets that include 213 million publications (1800-2020) and 7.6 million patents (1976-2020). We found that knowledge - which we conceptualize as the reduction of uncertainty in a knowledge network - grew linearly over time in naturally formed citation networks that themselves expanded exponentially. Moreover, our results revealed inflection points in the growth of knowledge that often corresponded to important developments within fields, such as major breakthroughs, new paradigms, or the emergence of entirely new areas of study. Around these inflection points, knowledge may grow rapidly or exponentially on a local scale, although the overall growth rate remains linear when viewed globally. Previous studies concluding an exponential growth of knowledge may have focused primarily on these local bursts of rapid growth around key developments, leading to the misconception of a global exponential trend. Our findings help to reconcile the discrepancy between the perceived exponential growth and the actual linear growth of knowledge by highlighting the distinction between local and global growth patterns. Overall, our findings reveal major science development trends for policymaking, showing that producing knowledge is far more challenging than producing papers.

physics.soc-ph↗

arXiv2023

Serendipity in Science

Serendipity plays an important role in scientific discovery. Indeed, many of the most important breakthroughs, ranging from penicillin to the electric battery, have been made by scientists who were stimulated by a chance exposure to unsought but useful information. However, not all scientists are equally likely to benefit from such serendipitous exposure. Although scholars generally agree that scientists with a prepared mind are most likely to benefit from serendipitous encounters, there is much less consensus over what precisely constitutes a prepared mind, with some research suggesting the importance of openness and others emphasizing the need for deep prior experience in a particular domain. In this paper, we empirically investigate the role of serendipity in science by leveraging a policy change that exogenously shifted the shelving location of journals in university libraries and subsequently exposed scientists to unsought scientific information. Using large-scale data on 2.4 million papers published in 9,750 journals by 520,000 scientists at 115 North American research universities, we find that scientists with greater openness are more likely to benefit from serendipitous encounters. Following the policy change, these scientists tended to cite less familiar and newer work, and ultimately published papers that were more innovative. By contrast, we find little effect on innovativeness for scientists with greater depth of experience, who, in our sample, tended to cite more familiar and older work following the policy change.

econ.EM↗

arXiv2022

Investigating individual writing style as a contributor to gender gaps in science and technology

Gender gaps in how scientific work is evaluated are well documented, but their sources remain debated. We ask whether an overlooked factor---the linguistic style of the writing itself---is gendered and consequential. Drawing on a framework that distinguishes informational features (which emphasize facts) from involved features (which emphasize relationships), we analyze single-authored abstracts of academic papers and patents across all fields of science and technology. Women's writing is systematically more involved than men's---richer in relational, audience-oriented features and higher in the balance of involved to informational language---a difference that holds across scientific fields, in collaborative as well as single-authored work, and in a large open-access biomedical corpus, throughout the full text of papers, not only their abstracts. This stylistic signature also shapes how work is received---papers whose abstracts are more involved are cited more by women and less by men, and the association persists even when papers are compared against their most content-similar alternatives, indicating a gendered signal independent of topic. That a near-costless feature of writing---word choice rather than substance---leaves a systematic, gendered trace on who cites scientific work points to a subtle channel through which evaluation bias may persist, in tension with the universalist ideal that ideas be judged independently of their author.

cs.CY↗

arXiv2022

Conceptual structure and the growth of scientific knowledge

How does scientific knowledge grow? This question has occupied a central place in the philosophy of science, stimulating heated debates, but yielding no clear consensus. Many explanations can be understood in terms of whether and how they view the expansion of knowledge as proceeding through the accretion of scientific concepts into larger conceptual structures. Here, we examine these views empirically, performing a large-scale analysis of the physical and social sciences, spanning five decades. Using natural language processing techniques, we create semantic networks of concepts, wherein noun phrases become linked when used in the same paper abstract. For both the physical and social sciences, we observe increasingly rigid conceptual cores (i.e., densely connected sets of highly central nodes) accompanied by the proliferation of periphery concepts (i.e., sparsely connected nodes that are highly connected to the core). Subsequently, we examine the relationship between conceptual structure and the growth of scientific knowledge, finding that scientific works are more innovative in fields with cores that have higher conceptual churn and with larger cores. Furthermore, scientific consensus is associated with reduced conceptual churn and fewer conceptual cores. Overall, our findings suggest that while the organization of scientific concepts is important for the growth of knowledge, the mechanisms vary across time.

cs.SI↗

arXiv2020

The Emergence of Higher-Order Structure in Scientific and Technological Knowledge Networks

The growth of science and technology is a recombinative process, wherein new discoveries and inventions are built from prior knowledge. Yet relatively little is known about the manner in which scientific and technological knowledge develop and coalesce into larger structures that enable or constrain future breakthroughs. Network science has recently emerged as a framework for measuring the structure and dynamics of knowledge. While helpful, existing approaches struggle to capture the global properties of the underlying networks, leading to conflicting observations about the nature of scientific and technological progress. We bridge this methodological gap using tools from algebraic topology to characterize the higher-order structure of knowledge networks in science and technology across scale. We observe rapid growth in the higher-order structure of knowledge in many scientific and technological fields. This growth is not observable using traditional network measures. We further demonstrate that the emergence of higher-order structure coincides with decline in lower-order structure, and has historically far outpaced the corresponding emergence of higher-order structure in scientific and technological collaboration networks. Up to a point, increases in higher-order structure are associated with better outcomes, as measured by the novelty and impact of papers and patents. However, the nature of science and technology produced under higher-order regimes also appears to be qualitatively different from that produced under lower-order ones, with the former exhibiting greater linguistic abstractness and greater tendencies for building upon prior streams of knowledge.

cs.SI↗

arXiv2012

A Dynamic Network Approach to Breakthrough Innovation

This paper outlines a framework for the study of innovation that treats discoveries as additions to evolving networks. As inventions enter they expand or limit the reach of the ideas they build on by influencing how successive discoveries use those ideas. The approach is grounded in novel measures of the extent to which an innovation amplifies or disrupts the status quo. Those measures index the effects inventions have on subsequent uses of prior discoveries. In so doing, they characterize a theoretically important but elusive feature of innovation. We validate our approach by showing it: (1) discriminates among innovations of similar impact in analyses of U.S. patents; (2) identifies discoveries that amplify and disrupt technology streams in select case studies; (3) implies disruptive patents decrease the use of their predecessors by 60% in difference-in-differences estimation; and, (4) yields novel findings in analyses of patenting at 110 U.S. universities.

cs.SI↗