- Article
- Published: 04 January 2023
Nature volume 613, pages 138–144 (2023)Cite this article
412k Accesses
603 Citations
4407 Altmetric
Abstract
Theories of scientific and technological change view discovery and invention as endogenous processes1,2, wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton’s …
- Article
- Published: 04 January 2023
Nature volume 613, pages 138–144 (2023)Cite this article
412k Accesses
603 Citations
4407 Altmetric
Abstract
Theories of scientific and technological change view discovery and invention as endogenous processes1,2, wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton’s words, ‘stand on the shoulders of giants’3,4,5,6,7. Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances8,9. Yet contrary to this view, studies suggest that progress is slowing in several major fields10,11. Here, we analyse these claims at scale across six decades, using data on 45 million papers and 3.9 million patents from six large-scale datasets, together with a new quantitative metric—the CD index12—that characterizes how papers and patents change networks of citations in science and technology. 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. This pattern holds universally across fields and is robust across multiple different citation- and text-based metrics1,13,14,15,16,17. Subsequently, we link this decline in disruptiveness to a narrowing in the use of previous 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.
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Data availability
Data associated with this study are freely available in a public repository at https://doi.org/10.5281/zenodo.7258379. Our study draws on data from six sources: the American Physical Society, JSTOR, Microsoft Academic Graph, Patents View, PubMed and WoS. Data from Microsoft Academic Graph, Patents View and PubMed are publicly available, and our repository includes complete data for analyses from these sources. Data from the American Physical Society, JSTOR and WoS are not publicly available, and were used under licence from their respective publishers. To facilitate replication, our repository includes limited versions of the data from these sources, which will enable calculation of basic descriptive statistics. The authors will make full versions of these data available upon request and with permission from their respective publishers. Source data are provided with this paper.
Code availability
Open-source code related to this study is available at https://doi.org/10.5281/zenodo.7258379 and http://www.cdindex.info. We used Python v.3.10.6 (pandas v.1.4.3, numpy v.1.23.1, matplotlib v.3.5.2, seaborn v.0.11.2, spacy v.2.2, jupyterlab v.3.4.4) to wrangle, analyse and visualize data and to conduct statistical analyses. We used MariaDB v.10.6.4 to wrangle data. We used R v.4.2.1 (ggplot2 v.3.36, ggrepel v.0.9.0) to visualize data. We used StataMP v.17.0 (reghdfe v.5.7.3) to conduct statistical analyses.
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Acknowledgements
This study was supported by the National Science Foundation (grant Nos. 1829168, 1932596 and 1829302).
Author information
Authors and Affiliations
Carlson School of Management, University of Minnesota, Minneapolis, MN, USA
Michael Park & Russell J. Funk 1.
School of Sociology, University of Arizona, Tucson, AZ, USA
Erin Leahey
Authors
- Michael Park
- Erin Leahey
- Russell J. Funk
Contributions
R.J.F. and E.L. collaboratively contributed to the conception and design of the study. R.J.F. and M.P. collaboratively contributed to the acquisition, analysis and interpretation of the data. R.J.F. created software used in the study. R.J.F., E.L. and M.P. collaboratively drafted and revised the manuscript.
Corresponding author
Correspondence to Russell J. Funk.
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Competing interests
The authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Distribution of CD5.
This figure gives an overview of the distribution of CD5 for papers (n = 24,659,076) and patents (n = 3,912,353). Panels a and c show counts of papers and patents over discrete intervals of CD5. Panels b and d show the distribution of CD5 over time, within 10 (papers) and 5 (patents) year intervals, using letter-value plots. These plots are similar to boxplots, but generally provide more reliable summaries for large datasets. They are drawn by identifying the median of the underlying distribution and then recursively drawing boxes outward from there in either direction that encompass half of the remaining data.
Extended Data Fig. 2 CD index measured using alternative forward citation windows.
This figure evaluates the sensitivity of our results to the use of different forward citation windows when computing the CD index for papers (n = 24,659,076) and patents (n = 3,912,353). In the main text, the index is computed based on citations made to papers and patents and their backward references as of 5 years after the year of publication. a and c plot the CD index using a longer, 10 year forward window, for papers and patents, respectively. b and d plot the CD index using all forward citations made to sample papers and patents as of the year 2017. Shaded bands correspond to 95% confidence intervals. Overall, the results mirror those reported in the main text, although the decline is somewhat steeper using longer forward citation windows, suggesting our primary results may represent a more conservative estimate.
Extended Data Fig. 3 Diversity of language use in science and technology over time.
This figure shows changes in the ratio of unique to total words (also known as the type-token ratio) over time based on data from the abstracts of papers (a, n = 76 WoS research area × year observations) and patents (b, n = 229 NBER technology category × year observations). For papers, lines correspond to WoS research areas; for patents, lines correspond to NBER technology categories. For paper abstracts, lines begin in 1992 because WoS does not reliably record abstracts for papers published prior to the early 1990s. The ratio of unique to total words is computed separately by field (i.e., the uniqueness of words and total word counts are determined within WoS research areas and NBER technology categories). If disruption is decreasing, we may plausibly expect to see a decrease in the diversity of words used by scientists and inventors, as discoveries and inventions will be less likely to create departures from the status quo, and will therefore be less likely to need to introduce new terminology. For both papers and patents, we observe declining diversity in word use over time, which is consistent with this expectation and corroborates our findings using the CD index.
Extended Data Fig. 4 Declining combinatorial novelty.
This figure shows changing patterns in the combinatorial novelty/conventionality of papers (a, n = 24,659,076) and patents (b, n = 3,912,353), using a previously proposed measure of “atypical combinations”14. The measure quantifies the degree to which the prior work cited by a paper or patent would be expected by chance. For papers, we follow prior work14 and consider combinations of cited journals. If a paper made three citations to prior work, and that work was