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Innovations are disproportionately likely in the periphery of a scientific network

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Abstract

The origins of innovation in science are typically understood using historical narratives that tend to be focused on small sets of influential authors, an approach that is rigorous but limited in scope. Here, we develop a framework for rigorously identifying innovation across an entire scientific field through automated analysis of a corpus of over 6000 documents that includes every paper published in the field of evolutionary medicine. This comprehensive approach allows us to explore statistical properties of innovation, asking where innovative ideas tend to originate within a field’s pre-existing conceptual framework. First, we develop a measure of innovation based on novelty and persistence, quantifying the collective acceptance of novel language and ideas. Second, we study the field’s conceptual landscape through a bibliographic coupling network. We find that innovations are disproportionately more likely in the periphery of the bibliographic coupling network, suggesting that the relative freedom allowed by remaining unconnected with well-established lines of research could be beneficial to creating novel and lasting change. In this way, the emergence of collective computation in scientific disciplines may have robustness–adaptability trade-offs that are similar to those found in other biosocial complex systems.

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References

  • Akre KL, Johnsen S (2014) Psychophysics and the evolution of behavior. Trends Ecol Evol 29(5):291–300

    Article  PubMed  Google Scholar 

  • Alcock J (2012) Emergence of evolutionary medicine: publication trends from 1991–2010. J Evol Med 1(1):c1-12

    Article  Google Scholar 

  • Amador SR, Pérez MD, López-Huertas MJ, Font RJR (2018) Indicator system for managing science, technology and innovation in universities. Scientometrics 115(3):1575–1587

    Article  Google Scholar 

  • Arendt D, Musser JM, Baker CVH, Bergman A, Cepko C, Erwin DH, Pavlicev M, Schlosser G, Widder S, Laubichler MD, Wagner GP (2016) The origin and evolution of cell types. Nat Rev Genet 17(12):744–757

    Article  CAS  PubMed  Google Scholar 

  • Baker P (2006) Baker-Brown Corpus. Edinburgh University Press, Edinburgh

    Google Scholar 

  • Baker P (2012) Acceptable bias? using corpus linguistics methods with critical discourse analysis. Critial Discouse Stud 9(3):247–256

    Article  Google Scholar 

  • Bassett DS, Wymbs NF, Puck Rombach M, Porter MA, Mucha PJ, Grafton ST (2013) Task-based core-periphery organization of human brain dynamics. PLoS Comput Biol 9(9):1–16

    Article  Google Scholar 

  • Biber D (2011) Corpus linguistics and the study of literature: back to the future. Scientif Stud Literature 1(1):15–23

    Article  Google Scholar 

  • Biscaro C, Giupponi C (2014) Co-authorship and bibliographic coupling network effects on citations. PLoS ONE 9(6):e99502

    Article  PubMed  PubMed Central  Google Scholar 

  • Bondi Marina, Scott Mike (2010) Keyness in Texts. John Benjamin’s Publishing, vol. 4 1 edition

  • Boyack KW, Klavans R (2010) Co-citation analysis, bibliographic coupling, and direct. J Am Soc Inform Sci Technol 61(12):2389–2404

    Article  Google Scholar 

  • Brosnan SF, Hopper LM (2014) Psychological limits on animal innovation. Anim Behav 92:325–332

    Article  Google Scholar 

  • Brozen Y (1951) Invention, innovation, and imitation. Am Econ Rev 41(2):239–257

    Google Scholar 

  • Cavalier-Smith T (2002) Origins of the machinery of recombination and sex. Heredity 88(2):125–141

    Article  CAS  PubMed  Google Scholar 

  • Chen C et al (2003) Visualizing scientific paradigms: an introduction. J Am Soc Inform Sci Technol 54(5):392–393

    Article  Google Scholar 

  • Colizza V, Alessandro Flammini M, Serrano A, Vespignani A (2006) Detecting rich-club ordering in complex networks. Nat Phys 2(February):110

    Article  CAS  Google Scholar 

  • Cressie N, Read TRC (1989) Pearson‘s X 2 and the loglikelihood ratio statistic G 2: a comparative review. Int Stat Rev/Revue Internationale de Statistique 57(1):19

    Google Scholar 

  • da Motta E, e Albuquerque, (2007) Inadequacy of technology and innovation systems at the periphery. Cambridge J Econom 31(5):669–690

  • Damerow Julia, Peirson BR Erick, Laubichler Manfred D (2017) The Giles Ecosystem – Storage, Text Extraction, and OCR of Documents. J Open Res Softw, 5(1)

  • Daniels BC, Krakauer DC, Flack JC (2017) Control of finite critical behaviour in a small-scale social system. Nat Commun 8:14301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Davidson EH, Erwin DH (2010) Evolutionary innovation and stability in animal gene networks. J Exp Zool B Mol Dev Evol 314 B(3):182–186

    Google Scholar 

  • De Quinn JB (2000) Outsourcing innovation: the new engine of growth. Sloan Management, Cambridge

    Google Scholar 

  • Doloreux D (2003) Regional innovation systems in the periphery: the case of the beauce in Québec (Canada). Int J Innov Manag 07(01):67–94

    Article  Google Scholar 

  • Ducatez S, Clavel J, Lefebvre L (2015) Ecological generalism and behavioural innovation in birds: technical intelligence or the simple incorporation of new foods? J Anim Ecol 84(1):79–89

    Article  PubMed  Google Scholar 

  • Fagerberg Jan (2004) Innovation: A guide to the literature. A guide to the literature. Georgia Institute of Technology, In Innovation

  • Fagerberg J, Mowery DC, Nelson RR et al (2005) The Oxford handbook of innovation. Oxford University Press, Oxford

    Google Scholar 

  • Ferreira FAF (2018) Mapping the field of arts-based management: bibliographic coupling and co-citation analyses. J Bus Res 85:348–357

    Article  Google Scholar 

  • Fitjar RD, Rodríguez-Pose AS (2011) Innovating in the periphery: firms, values and innovation in Southwest Norway. Eur Plan Stud 19(4):555–574

    Article  Google Scholar 

  • Gluckman PD, Beedle AS, Hanson MA (2009) Principles of Evolutionary Medicine, 1st edn. Oxford University Press, Oxford

    Google Scholar 

  • Gollo Leonardo L, Zalesky Andrew, Hutchison R Matthew, van den Heuvel Martijn, Breakspear Michael (2015) Dwelling quietly in the rich club: Brain network determinants of slow cortical fluctuations. Philosophic Trans Royal Soc B Biol Sci, 370(1668)

  • He JJ, Tian X (2018) Finance and corporate innovation: a survey. Asia Pac J Financ Stud 47(2):165–212

    Article  Google Scholar 

  • Holland PW, Garcia-Fernàndez J, Williams NA, Sidow A (1994) Gene duplications and the origins of vertebrate development. Development (Cambridge, England). Supplement 1994:125–133

  • Jarneving B (2007) Bibliographic coupling and its application to research-front and other core documents. J Informet 1(4):287–307

    Article  Google Scholar 

  • Kessler MM (1963) Bibliographic coupling between scientific papers. Am Doc 14(1):10–25

    Article  Google Scholar 

  • Kim Joochul (2015) A University’s Role for Regional Innovation : Arizona Universities’ Contribution to Regional Economic Growth. World Technopolis Review, pages 79–86

  • Kryukov V, Gorin A (2016) Digital technologies as education innovation at universities. J Internet Banking , 21(3)

  • Kuhn Thomas S (1962) The structure of scientific revolutions. Philos Rhetoric Sci, page 65

  • Kuusi O, Meyer M (2007) Anticipating technological breakthroughs: Using bibliographic coupling to explore the nanotubes paradigm. Scientometrics 70(3):759–777

    Article  CAS  Google Scholar 

  • Le Phi K, Ho CN, Nguyen RA, Miles MP, Bonney L (2017) Exploring market orientation, innovation, and financial performance in agricultural value chains in emerging economies. J Innov Knowl 3(3):154–163

    Google Scholar 

  • McAuley Julian J, Da Fontoura Costa Luciano, Caetano Tibrio S (2007) Rich-club phenomenon across complex network hierarchies. Appl Phys Lett, 91(8)

  • Muller GB, Wagner GP (1991) Novelty in evolution: restructuring the concept. Annu Rev Ecol Syst 22(1):229–256

    Article  Google Scholar 

  • Naranjo-Valencia JC, Calderon-Hernandez G, Jimenez-Jimenez D, Sanz-Valle R (2018) Entrepreneurship and Innovation: Evidence in Colombian SMEs. In: Handbook of Research on Intrapreneurship and Organizational Sustainability in SMEs, pages 294–316. IGI Global

  • Nesse Randolph M (2018) Internation Society for Evolution. Medicine & Public Health

  • Nesse Randolph M, Bergstrom Carl T, Ellison Peter T, Flier Jeffrey S, Gluckman Peter D, Govindaraju Diddahally R, Niethammer Dietrich, Omenn Gilbert S, Perlman Robert L, Schwartz Mark D, Thomas Mark G, Stearns Stephen C, Valle David (2009) Making evolutionary biology a basic science for medicine. PNAS, pages 1–8

  • Nesse Randolph M, Williams George C (1994) Why We Get Sick: the new science of Darwinian medicine. Times Books, New York, 1st edition

  • Nettle D, Frankenhuis WE (2019) The evolution of life-history theory: a bibliometric analysis of an interdisciplinary research area. Proc R Soc B 286(1899):20190040

    Article  PubMed  PubMed Central  Google Scholar 

  • Nigam S, Shimono M, Ito S, Yeh F-C, Timme N, Myroshnychenko M, Lapish CC, Tosi Z, Hottowy P, Smith WC, Masmanidis SC, Litke AM, Sporns O, Beggs JM (2016) Rich-club organization in effective connectivity among cortical neurons. J Neurosci 36(3):670–684

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Opsahl T, Colizza V, Panzarasa P, Ramasco JJ (2008) Prominence and control: the weighted rich-club effect. Phys Rev Lett 101(16):1–4

    Article  Google Scholar 

  • Painter Deryc T, Damerow Julia, Laubichler Manfred D (2019) Evoltuion of evolutionary medicine. In: The Dynamics of Science: Computational Frontiers in the History and Philosophy of Science, pages 123–138. Pittsburgh University Press; Pittsburgh

  • Painter DT, Daniels BC, Jost J (2019) Network analysis for the digital humanities: principles, problems, extensions. Isis 110(3):538–554

    Article  Google Scholar 

  • Peter Gogarten J, Townsend JP (2005) Horizontal gene transfer, genome innovation and evolution. Nat Rev Microbiol 3(9):679–687

    Article  PubMed  Google Scholar 

  • Quandt EM, Deatherage DE, Ellington AD, Georgiou G, Barrick JE (2014) Recursive genomewide recombination and sequencing reveals a key refinement step in the evolution of a metabolic innovation in escherichia coli. Proc Natl Acad Sci 111(6):2217–2222

    Article  CAS  PubMed  Google Scholar 

  • Ramsey G, Bastian ML, Van Schaik C (2007) Animal innovation de ned and operationalized. Innovation 30(4):393–437

    Google Scholar 

  • Rondi E, De Massis A, Kotlar J (2018) Unlocking innovation potential: a typology of family business innovation postures and the critical role of the family system. J Fam Bus Strat 15(2):0–1

    Google Scholar 

  • Scott M (1999) Wordsmith Tools

  • Shane S (1993) Cultural influences on national rates of innovation. J Bus Ventur 8(1):59–73

    Article  Google Scholar 

  • Small H (1997) Update on science mapping: creating large document spaces. Scientometrics 38(2):275–293

    Article  Google Scholar 

  • Small H (2003) Paradigms, citations, and maps of science: a personal history. J Am Soc Inform Sci Technol 54(5):394–399

    Article  Google Scholar 

  • Szirmai A, Naude W, Goedhuys M (e2011) Entrepreneurship, innovation, and economic development. Oxford University Press, Oxford

    Book  Google Scholar 

  • Thompson Reuters. Web of Science, 2012

  • Trevathan WR, Smith EO, McKenna JJ (1999) Evolutionary Medicine. Oxford University Press On Demand, Oxford

    Google Scholar 

  • van den Heuvel MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31(44):15775–15786

    Article  PubMed  PubMed Central  Google Scholar 

  • Wagner GP (2014) Homology, Gene, and Evolutionary Innovation. Princeton University Press, USA

    Book  Google Scholar 

  • Wagner GP (2018) Homology, genes, and evolutionary innovation. Princeton University Press, USA

    Google Scholar 

  • Wagner GP, Altenberg L (1996) Perspective: complex adaptations and the evolution of evolvability. Evolution 50(3):967–976

    Article  PubMed  Google Scholar 

  • Werren JH (2011) Selfish genetic elements, genetic conflict, and evolutionary innovation. Proc Natl Acad Sci 108(Supplement-2):10863–10870

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Williams GC, Nesse RM (1991) The dawn of Darwinian medicine. Q R Biol 66(1):1–22

    Article  CAS  Google Scholar 

  • Wong PK, Ho YP, Autio E (2005) Entrepreneurship, innovation and economic growth: evidence from GEM data. Small Bus Econ 24(3):335–350

    Article  Google Scholar 

  • Yan E, Ding Y (2012) Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. J Am Soc Inform Sci Technol 63(7):1313–1326

    Article  Google Scholar 

  • Zhao D, Strotmann A (2008) Evolution of research activities and intellectual influences in information science 1996–2005: introducing author bibliographic-coupling analysis. J Am Soc Inform Sci Technol 59(13):80-2070–2086

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Correspondence to Manfred D. Laubichler.

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Painter, D.T., Daniels, B.C. & Laubichler, M.D. Innovations are disproportionately likely in the periphery of a scientific network. Theory Biosci. 140, 391–399 (2021). https://doi.org/10.1007/s12064-021-00359-1

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