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arxiv: 2509.10389 · v2 · submitted 2025-09-12 · 💻 cs.DL · cs.SI

Beginner's Charm: Beginner-Heavy Teams Are Associated With High Scientific Disruption

Mahdee Mushfique Kamal , Raiyan Abdul Baten This is my paper

Pith reviewed 2026-05-18 17:45 UTC · model grok-4.3

classification 💻 cs.DL cs.SI
keywords research teamsbeginnersscientific disruptioninnovationcollaborationcitation analysisknowledge recombination
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The pith

Teams with higher fractions of absolute beginners produce more disruptive science by recombining less canonical ideas.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper analyzes 29 million articles published between 1941 and 2020 to establish that teams containing more authors with zero prior publications generate systematically more disruptive and innovative outputs. This pattern holds across disciplines and team sizes. Beginner-heavy teams integrate knowledge differently, referencing broader and less standard prior work while forming more atypical combinations. The benefit grows when beginners collaborate with early-career researchers or co-authors who themselves have disruptive records. Although disruptive papers usually receive fewer citations overall, the most disruptive outputs from beginner-heavy teams still attract high citation counts.

Core claim

Teams with a higher fraction of beginners are systematically more disruptive and innovative. Their contributions link to distinct knowledge-integration behaviors, including drawing on broader and less canonical prior work and producing more atypical recombinations. Collaboration structure further shapes outcomes: disruption is high when beginners work with early-career colleagues or with co-authors who have disruptive track records. Although disruption and citations are negatively correlated overall, highly disruptive papers from beginner-heavy teams are highly cited.

What carries the argument

The fraction of beginner authors in a team, defined as authors with zero prior publications, which predicts higher paper disruption via atypical knowledge recombination.

Load-bearing premise

The disruption metric and the definition of beginners as authors with no prior publications measure genuine novelty rather than artifacts of citation databases or career-stage tracking choices.

What would settle it

Re-running the analysis on an independent corpus or with an alternative novelty measure such as text-based semantic distance shows the positive link between beginner fraction and disruption disappearing.

read the original abstract

Teams now drive most scientific advances, yet the impact of absolute beginners -- authors with no prior publications -- remains understudied. Analyzing over 29 million articles published between 1941 and 2020 across disciplines and team sizes, we uncover a near-universal and previously undocumented pattern: teams with a higher fraction of beginners are systematically more disruptive and innovative. Their contributions are linked to distinct knowledge-integration behaviors, including drawing on broader and less canonical prior work and producing more atypical recombinations. Collaboration structure further shapes outcomes: disruption is high when beginners work with early-career colleagues or with co-authors who have disruptive track records. Although disruption and citations are negatively correlated overall, highly disruptive papers from beginner-heavy teams are highly cited. These findings reveal a ``beginner's charm'' in science, highlighting the underrecognized yet powerful value of beginner fractions in teams and suggesting actionable strategies for fostering a thriving ecosystem of innovation in science and technology.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript analyzes over 29 million articles (1941–2020) across disciplines and finds a near-universal positive association between the fraction of absolute beginners (authors with zero prior publications in the corpus) in a team and the paper's disruption score. This pattern is linked to distinct behaviors including broader and less canonical references plus atypical recombinations; collaboration structure (e.g., pairing with early-career or disruptive-track-record co-authors) moderates the effect, and highly disruptive beginner-heavy papers remain highly cited despite the overall negative disruption-citation correlation.

Significance. If the central association proves robust after addressing measurement and selection issues, the result would be significant for the literature on team composition and scientific innovation. It provides large-scale observational evidence with controls for team size and discipline, identifies plausible behavioral mediators, and yields actionable implications for team formation. The scale of the corpus and the falsifiable predictions about reference atypicality strengthen its potential contribution.

major comments (2)
  1. [Methods] Methods section: The operationalization of beginners as authors with zero prior publications in the 29M-article corpus is load-bearing for the entire analysis. Name disambiguation errors, incomplete coverage of pre-1941 or non-indexed work, and field-specific publication norms can systematically mislabel true beginners, potentially generating the reported association with disruption scores rather than reflecting genuine novelty. The paper must provide robustness checks (alternative thresholds, external validation samples, or field-specific sensitivity tests) before the 'near-universal pattern' claim can be accepted.
  2. [Methods] Methods section: The disruption index relies on forward-citation patterns whose computation is sensitive to citation-window length and database coverage. Papers with atypical reference lists (one of the proposed mediators) may receive systematically different citation dynamics, creating a mechanical link between the beginner label and the outcome. The manuscript should demonstrate that the association survives alternative disruption metrics or explicit controls for reference-list atypicality measured independently of the disruption score.
minor comments (2)
  1. [Abstract] Abstract and introduction: The phrase 'near-universal' should be qualified with the exact set of controls and disciplines for which the association holds after the main regressions.
  2. [Discussion] The manuscript would benefit from an explicit comparison table placing the beginner-fraction effect sizes against prior work on team experience and disruption to clarify incremental contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. We have addressed each major comment below and will incorporate the suggested robustness checks and additional analyses in the revised version to strengthen the claims.

read point-by-point responses

  1. Referee: [Methods] Methods section: The operationalization of beginners as authors with zero prior publications in the 29M-article corpus is load-bearing for the entire analysis. Name disambiguation errors, incomplete coverage of pre-1941 or non-indexed work, and field-specific publication norms can systematically mislabel true beginners, potentially generating the reported association with disruption scores rather than reflecting genuine novelty. The paper must provide robustness checks (alternative thresholds, external validation samples, or field-specific sensitivity tests) before the 'near-universal pattern' claim can be accepted.

    Authors: We agree that the beginner definition is central and that name disambiguation errors or corpus limitations could introduce bias. In the revision we will add robustness checks using alternative thresholds (e.g., authors with 0–2 prior publications) and field-specific sensitivity tests across disciplines. We will also expand the limitations discussion on pre-1941 coverage and non-indexed work. While obtaining large-scale external validation samples is not feasible with current data, the consistency of results across disciplines and team sizes offers supporting evidence against systematic mislabeling. revision: yes

  2. Referee: [Methods] Methods section: The disruption index relies on forward-citation patterns whose computation is sensitive to citation-window length and database coverage. Papers with atypical reference lists (one of the proposed mediators) may receive systematically different citation dynamics, creating a mechanical link between the beginner label and the outcome. The manuscript should demonstrate that the association survives alternative disruption metrics or explicit controls for reference-list atypicality measured independently of the disruption score.

    Authors: We acknowledge the potential sensitivity of the disruption index and the risk of mechanical links. The revised manuscript will include analyses with alternative disruption metrics using different citation windows and database subsets. We will also add explicit controls for reference-list atypicality (using independent measures such as reference diversity and canonicality scores) in the main regressions to confirm the beginner-disruption association persists independently of these factors. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical associations from independent measures

full rationale

The paper conducts a large-scale observational analysis on 29 million articles (1941–2020). Beginners are operationalized as authors with zero prior publications in the corpus, and disruption is computed from forward-citation patterns (standard index). These two variables are defined and measured independently; the reported associations, regressions, and mechanism tests (broader references, atypical recombinations) are statistical outputs from the data rather than reductions by construction. No equations, self-citations, or fitted parameters are shown to make the central claim tautological. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The analysis rests on standard scientometric assumptions about citation data completeness and the validity of the disruption index; no new entities are postulated, but several operational thresholds (e.g., definition of absolute beginner, time windows for prior work) function as free parameters.

free parameters (2)
  • beginner threshold
    Zero prior publications is treated as the cutoff; any other cutoff would change the fraction variable.
  • disruption index parameters
    The paper inherits the standard disruption metric parameters from prior work without re-deriving them.
axioms (2)
  • domain assumption Citation databases accurately capture all prior publications for identifying absolute beginners.
    Invoked when labeling authors as beginners across the 1941-2020 corpus.
  • domain assumption The disruption metric validly measures scientific innovation independent of team composition.
    Central to interpreting higher scores as evidence of beginner charm.

pith-pipeline@v0.9.0 · 5691 in / 1409 out tokens · 29700 ms · 2026-05-18T17:45:42.692905+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

94 extracted references · 94 canonical work pages

  1. [1]

    Management Science47(1), 117–132 (2001)

    Fleming, L.: Recombinant uncertainty in technological search. Management Science47(1), 117–132 (2001)

    work page 2001

  2. [2]

    Research Policy40(10), 1321–1331 (2011)

    Schilling, M.A., Green, E.: Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences. Research Policy40(10), 1321–1331 (2011)

    work page 2011

  3. [3]

    Fleming, L., Singh, J.: Lone inventors as sources of breakthroughs: Myth or reality? Management Science56(1), 41–56 (2010)

    work page 2010

  4. [4]

    Goldilocks

    Baten, R.A., Aslin, R.N., Ghoshal, G., Hoque, E.: Novel idea generation in social networks is optimized by exposure to a “Goldilocks” level of idea-variability. PNAS Nexus1(5), 255 (2022) https://doi.org/10.1093/pnasnexus/pgac255 https://academic.oup.com/pnasnexus/article- pdf/1/5/pgac255/47815796/pgac255.pdf

    work page doi:10.1093/pnasnexus/pgac255 2022

  5. [5]

    Scientific Reports11(1), 10261 (2021)

    Baten, R.A., Aslin, R.N., Ghoshal, G., Hoque, E.: Cues to gender and racial identity reduce creativity in diverse social networks. Scientific Reports11(1), 10261 (2021)

    work page 2021

  6. [6]

    Journal of the Royal Society Interface17(171), 20200667 (2020)

    Baten, R.A., Bagley, D., Tenesaca, A., Clark, F., Bagrow, J.P., Ghoshal, G., Hoque, E.: Creativity in temporal social networks: How divergent thinking is impacted by one’s choice of peers. Journal of the Royal Society Interface17(171), 20200667 (2020)

    work page 2020

  7. [7]

    Nature Humanities and Social Sciences Communications12(1), 1–13 (2025)

    Kelty, S., Baten, R.A., Proma, A.M., Hoque, E., Bollen, J., Ghoshal, G.: The innovation trade- off: How following superstars shapes academic novelty. Nature Humanities and Social Sciences Communications12(1), 1–13 (2025)

    work page 2025

  8. [8]

    arXiv preprint arXiv:2410.15264 (2024)

    Baten, R.A., Bangash, A.S., Veera, K., Ghoshal, G., Hoque, E.: AI can enhance creativity in social networks. arXiv preprint arXiv:2410.15264 (2024)

    work page arXiv 2024

  9. [9]

    Proceedings of the National Academy of Sciences113(41), 11483–11488 (2016)

    Acemoglu, D., Akcigit, U., Kerr, W.R.: Innovation network. Proceedings of the National Academy of Sciences113(41), 11483–11488 (2016)

    work page 2016

  10. [10]

    Science Advances3(4), 1601315 (2017)

    Mukherjee, S., Romero, D.M., Jones, B., Uzzi, B.: The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: The hotspot. Science Advances3(4), 1601315 (2017)

    work page 2017

  11. [11]

    Kuhn, T.S.: The Structure of Scientific Revolutions vol. 962. University of Chicago Press, Chicago, ??? (1997)

    work page 1997

  12. [12]

    Research Policy46(8), 1416–1436 (2017)

    Wang, J., Veugelers, R., Stephan, P.: Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy46(8), 1416–1436 (2017)

    work page 2017

  13. [13]

    Research Policy44(3), 684–697 (2015)

    Lee, Y.-N., Walsh, J.P., Wang, J.: Creativity in scientific teams: Unpacking novelty and impact. Research Policy44(3), 684–697 (2015)

    work page 2015

  14. [14]

    death of the renaissance man

    Jones, B.F.: The burden of knowledge and the “death of the renaissance man”: Is innovation getting harder? The Review of Economic Studies76(1), 283–317 (2009)

    work page 2009

  15. [15]

    Proceedings of the National Academy of Sciences108(47), 18910–18914 (2011)

    Jones, B.F., Weinberg, B.A.: Age dynamics in scientific creativity. Proceedings of the National Academy of Sciences108(47), 18910–18914 (2011)

    work page 2011

  16. [16]

    Minerva31(1), 1–20 (1993)

    Rappa, M., Debackere, K.: Youth and scientific innovation: The role of young scientists in the development of a new field. Minerva31(1), 1–20 (1993)

    work page 1993

  17. [17]

    Journal of Human Capital 13(2), 341–373 (2019) 29

    Packalen, M., Bhattacharya, J.: Age and the trying out of new ideas. Journal of Human Capital 13(2), 341–373 (2019) 29

    work page 2019

  18. [18]

    Harvard University Press, ??? (1985)

    Cohen, I.B.: Revolution in Science. Harvard University Press, ??? (1985)

    work page 1985

  19. [19]

    Trends in Cognitive Sciences25(12), 1058–1071 (2021)

    Spreng, R.N., Turner, G.R.: From exploration to exploitation: A shifting mental mode in late life development. Trends in Cognitive Sciences25(12), 1058–1071 (2021)

    work page 2021

  20. [20]

    Royal Society Open Science6(11), 191255 (2019)

    Livan, G.: Don’t follow the leader: How ranking performance reduces meritocracy. Royal Society Open Science6(11), 191255 (2019)

    work page 2019

  21. [21]

    Science359(6379), 0185 (2018)

    Fortunato, S., Bergstrom, C.T., B¨ orner, K., Evans, J.A., Helbing, D., Milojevi´ c, S., Petersen, A.M., Radicchi, F., Sinatra, R., Uzzi, B.,et al.: Science of science. Science359(6379), 0185 (2018)

    work page 2018

  22. [22]

    Wiley Online Library, ??? (2014)

    Simonton, D.K.: The Wiley Handbook of Genius. Wiley Online Library, ??? (2014)

    work page 2014

  23. [23]

    Scientific Reports14(1), 28172 (2024)

    Higashide, N., Miura, T., Tomokiyo, Y., Asatani, K., Sakata, I.: Mid-career pitfall of consecutive success in science. Scientific Reports14(1), 28172 (2024)

    work page 2024

  24. [24]

    Research Management Review22(1), 1 (2017)

    Spencer, T., Scott, J.: Research administrative burden: A qualitative study of local variations and relational effects. Research Management Review22(1), 1 (2017)

    work page 2017

  25. [25]

    Palgrave Macmil- lan, ??? (2018)

    McAlpine, L., Amundsen, C.: Identity-Trajectories of Early Career Researchers. Palgrave Macmil- lan, ??? (2018)

    work page 2018

  26. [26]

    F1000Research7, 1605 (2018)

    Schimanski, L.A., Alperin, J.P.: The evaluation of scholarship in academic promotion and tenure processes: Past, present, and future. F1000Research7, 1605 (2018)

    work page 2018

  27. [27]

    arXiv preprint arXiv:2202.04044 (2022)

    Cui, H., Wu, L., Evans, J.A.: Aging scientists and slowed advance. arXiv preprint arXiv:2202.04044 (2022)

    work page arXiv 2022

  28. [28]

    University of Chicago Press, ??? (1973)

    Merton, R.K.: The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago Press, ??? (1973)

    work page 1973

  29. [29]

    American Psychologist76(6), 1067 (2021)

    Candia, C., Uzzi, B.: Quantifying the selective forgetting and integration of ideas in science and technology. American Psychologist76(6), 1067 (2021)

    work page 2021

  30. [30]

    arXiv preprint arXiv:2504.04677 (2025)

    Lin, Y., Li, L., Wu, L.: The disruption index measures displacement between a paper and its most cited reference. arXiv preprint arXiv:2504.04677 (2025)

    work page arXiv 2025

  31. [31]

    In: Proceedings of Cognitiva 85, Paris, France, pp

    LeCun, Y.: Une proc´ edure d’apprentissage pour r´ eseau ` a seuil asym´ etrique (a learning scheme for asymmetric threshold networks). In: Proceedings of Cognitiva 85, Paris, France, pp. 599–604 (1985)

    work page 1985

  32. [32]

    http://yann.lecun.com/exdb/publis/index.html

    LeCun, Y.: Yann LeCun’s Publications. http://yann.lecun.com/exdb/publis/index.html. Accessed on August 31, 2025 (2025)

    work page 2025

  33. [33]

    Scientometrics129(10), 6127–6148 (2024)

    Yang, A.J., Xu, H., Ding, Y., Liu, M.: Unveiling the dynamics of team age structure and its impact on scientific innovation. Scientometrics129(10), 6127–6148 (2024)

    work page 2024

  34. [34]

    Nature Human Behaviour5(10), 1314–1322 (2021)

    Zeng, A., Fan, Y., Di, Z., Wang, Y., Havlin, S.: Fresh teams are associated with original and multidisciplinary research. Nature Human Behaviour5(10), 1314–1322 (2021)

    work page 2021

  35. [35]

    Scientific Data10(1), 315 (2023)

    Lin, Z., Yin, Y., Liu, L., Wang, D.: SciSciNet: A large-scale open data lake for the science of science research. Scientific Data10(1), 315 (2023)

    work page 2023

  36. [36]

    https:// northwestern-cssi.github.io/sciscinet/

    Center for Science of Science and Innovation (CSSI), Kellogg School of Management, North- western University: SciSciNet-v2: A Linked Dataset for Science of Science Research. https:// northwestern-cssi.github.io/sciscinet/. Accessed: 2025-08-26 (2025)

    work page 2025

  37. [37]

    ACM Transactions on Knowledge Discovery from Data (TKDD)3(3), 1–29 (2009) 30

    Torvik, V.I., Smalheiser, N.R.: Author name disambiguation in medline. ACM Transactions on Knowledge Discovery from Data (TKDD)3(3), 1–29 (2009) 30

    work page 2009

  38. [38]

    Learned Publishing37(4), 1621 (2024)

    Frandsen, T.F., Nicolaisen, J.: Defining the early career researcher: A study of publication-based definitions. Learned Publishing37(4), 1621 (2024)

    work page 2024

  39. [39]

    PLoS One16(9), 0257141 (2021)

    Bradshaw, C.J., Chalker, J.M., Crabtree, S.A., Eijkelkamp, B.A., Long, J.A., Smith, J.R., Trinajs- tic, K., Weisbecker, V.: A fairer way to compare researchers at any career stage and in any discipline using open-access citation data. PLoS One16(9), 0257141 (2021)

    work page 2021

  40. [40]

    Revista de Gest˜ ao30(1), 62–77 (2023)

    Vilela, N.G.S., Casado, T.: Career stages in management studies: A systematic review of scientific production from 2011 to 2020. Revista de Gest˜ ao30(1), 62–77 (2023)

    work page 2011

  41. [41]

    Scientometrics58(1), 49–90 (2003)

    Bonaccorsi, A., Daraio, C.: Age effects in scientific productivity. Scientometrics58(1), 49–90 (2003)

    work page 2003

  42. [42]

    Management Science63(3), 791–817 (2017)

    Funk, R.J., Owen-Smith, J.: A dynamic network measure of technological change. Management Science63(3), 791–817 (2017)

    work page 2017

  43. [43]

    Nature566(7744), 378–382 (2019)

    Wu, L., Wang, D., Evans, J.A.: Large teams develop and small teams disrupt science and technology. Nature566(7744), 378–382 (2019)

    work page 2019

  44. [44]

    Scientometrics129(1), 601–639 (2024)

    Leibel, C., Bornmann, L.: What do we know about the disruption index in scientometrics? An overview of the literature. Scientometrics129(1), 601–639 (2024)

    work page 2024

  45. [45]

    Proceedings of the National Academy of Sciences118(41), 2021636118 (2021)

    Chu, J.S., Evans, J.A.: Slowed canonical progress in large fields of science. Proceedings of the National Academy of Sciences118(41), 2021636118 (2021)

    work page 2021

  46. [46]

    Nature 613(7942), 138–144 (2023)

    Park, M., Leahey, E., Funk, R.J.: Papers and patents are becoming less disruptive over time. Nature 613(7942), 138–144 (2023)

    work page 2023

  47. [47]

    Information Processing & Management60(3), 103252 (2023)

    Wei, C., Li, J., Shi, D.: Quantifying revolutionary discoveries: Evidence from Nobel Prize-winning papers. Information Processing & Management60(3), 103252 (2023)

    work page 2023

  48. [48]

    Harper Business, New York (2011)

    Christensen, C.M.: The Innovator’s Dilemma: The Revolutionary Book That Will Change the Way You Do Business. Harper Business, New York (2011)

    work page 2011

  49. [49]

    Psychological Science23(3), 219–224 (2012)

    Minson, J.A., Mueller, J.S.: The cost of collaboration: Why joint decision making exacerbates rejection of outside information. Psychological Science23(3), 219–224 (2012)

    work page 2012

  50. [50]

    Information Systems Research27(3), 618–635 (2016)

    Greenstein, S., Zhu, F.: Open content, Linus’ law, and neutral point of view. Information Systems Research27(3), 618–635 (2016)

    work page 2016

  51. [51]

    Small Group Research44(3), 332–352 (2013)

    Paulus, P.B., Kohn, N.W., Arditti, L.E., Korde, R.M.: Understanding the group size effect in electronic brainstorming. Small Group Research44(3), 332–352 (2013)

    work page 2013

  52. [52]

    Nature Biotechnology31(2), 108–111 (2013)

    Lakhani, K.R., Boudreau, K.J., Loh, P.-R., Backstrom, L., Baldwin, C., Lonstein, E., Lydon, M., MacCormack, A., Arnaout, R.A., Guinan, E.C.: Prize-based contests can provide solutions to computational biology problems. Nature Biotechnology31(2), 108–111 (2013)

    work page 2013

  53. [53]

    Science 342(6157), 468–472 (2013)

    Uzzi, B., Mukherjee, S., Stringer, M., Jones, B.: Atypical combinations and scientific impact. Science 342(6157), 468–472 (2013)

    work page 2013

  54. [54]

    Science308(5722), 697–702 (2005)

    Guimera, R., Uzzi, B., Spiro, J., Amaral, L.A.N.: Team assembly mechanisms determine collabo- ration network structure and team performance. Science308(5722), 697–702 (2005)

    work page 2005

  55. [55]

    Science322(5905), 1259–1262 (2008)

    Jones, B.F., Wuchty, S., Uzzi, B.: Multi-university research teams: Shifting impact, geography, and stratification in science. Science322(5905), 1259–1262 (2008)

    work page 2008

  56. [56]

    Proceedings of the National Academy of Sciences121(21), 2322462121 (2024) 31

    Li, H., Tessone, C.J., Zeng, A.: Productive scientists are associated with lower disruption in scientific publishing. Proceedings of the National Academy of Sciences121(21), 2322462121 (2024) 31

    work page 2024

  57. [57]

    Journal of Informetrics16(3), 101321 (2022)

    Li, H., Wu, M., Wang, Y., Zeng, A.: Bibliographic coupling networks reveal the advantage of diversification in scientific projects. Journal of Informetrics16(3), 101321 (2022)

    work page 2022

  58. [58]

    PLoS One12(6), 0178074 (2017)

    Michalska-Smith, M.J., Allesina, S.: And, not or: Quality, quantity in scientific publishing. PLoS One12(6), 0178074 (2017)

    work page 2017

  59. [59]

    Scientific Reports15(1), 10812 (2025)

    Li, M., Livan, G., Righi, S.: Quantifying the dynamics of peak disruption in scientific careers. Scientific Reports15(1), 10812 (2025)

    work page 2025

  60. [60]

    Science354(6312), 5239 (2016)

    Sinatra, R., Wang, D., Deville, P., Song, C., Barab´ asi, A.-L.: Quantifying the evolution of individual scientific impact. Science354(6312), 5239 (2016)

    work page 2016

  61. [61]

    PLoS One19(12), 0313268 (2024)

    Li, M., Livan, G., Righi, S.: Breaking down the relationship between disruption scores and citation counts. PLoS One19(12), 0313268 (2024)

    work page 2024

  62. [62]

    arXiv preprint arXiv:2305.03589 (2023)

    Zeng, A., Fan, Y., Di, Z., Wang, Y., Havlin, S.: Disruptive papers in science are losing impact. arXiv preprint arXiv:2305.03589 (2023)

    work page arXiv 2023

  63. [63]

    Science316(5827), 1036–1039 (2007)

    Wuchty, S., Jones, B.F., Uzzi, B.: The increasing dominance of teams in production of knowledge. Science316(5827), 1036–1039 (2007)

    work page 2007

  64. [64]

    American Journal of Sociology111(2), 447–504 (2005)

    Uzzi, B., Spiro, J.: Collaboration and creativity: The small world problem. American Journal of Sociology111(2), 447–504 (2005)

    work page 2005

  65. [65]

    small-world

    Schilling, M.A.: A “small-world” network model of cognitive insight. Creativity Research Journal 17(2-3), 131–154 (2005)

    work page 2005

  66. [66]

    Research Policy 39(8), 1051–1059 (2010)

    Schoenmakers, W., Duysters, G.: The technological origins of radical inventions. Research Policy 39(8), 1051–1059 (2010)

    work page 2010

  67. [67]

    Journal of Product Innovation Management30(6), 1212–1226 (2013)

    Kelley, D.J., Ali, A., Zahra, S.A.: Where do breakthroughs come from? Characteristics of high- potential inventions. Journal of Product Innovation Management30(6), 1212–1226 (2013)

    work page 2013

  68. [68]

    Journal of Informetrics7(4), 861–873 (2013)

    Didegah, F., Thelwall, M.: Which factors help authors produce the highest impact research? Collaboration, journal and document properties. Journal of Informetrics7(4), 861–873 (2013)

    work page 2013

  69. [69]

    Scientometrics126(1), 785–799 (2021)

    Mammola, S., Fontaneto, D., Mart´ ınez, A., Chichorro, F.: Impact of the reference list features on the number of citations. Scientometrics126(1), 785–799 (2021)

    work page 2021

  70. [70]

    Proceedings of the National Academy of Sciences117(17), 9284–9291 (2020)

    Hofstra, B., Kulkarni, V.V., Munoz-Najar Galvez, S., He, B., Jurafsky, D., McFarland, D.A.: The diversity–innovation paradox in science. Proceedings of the National Academy of Sciences117(17), 9284–9291 (2020)

    work page 2020

  71. [71]

    Studies in Higher Education49(11), 2036–2051 (2024)

    Kwiek, M., Roszka, W.: The young and the old, the fast and the slow: A large-scale study of productivity classes and rank advancement. Studies in Higher Education49(11), 2036–2051 (2024)

    work page 2036

  72. [72]

    Strategic Management Journal17(S2), 109–122 (1996)

    Grant, R.M.: Toward a knowledge-based theory of the firm. Strategic Management Journal17(S2), 109–122 (1996)

    work page 1996

  73. [73]

    Journal of Organizational Behavior32(2), 264–290 (2011)

    Kunze, F., Boehm, S.A., Bruch, H.: Age diversity, age discrimination climate and performance consequences—a cross organizational study. Journal of Organizational Behavior32(2), 264–290 (2011)

    work page 2011

  74. [74]

    Journal of Managerial Psychology31(1), 2–17 (2016)

    Schneid, M., Isidor, R., Steinmetz, H., Kabst, R.: Age diversity and team outcomes: A quantitative review. Journal of Managerial Psychology31(1), 2–17 (2016)

    work page 2016

  75. [75]

    Journal of Applied 32 Psychology106(1), 71 (2021)

    Li, Y., Gong, Y., Burmeister, A., Wang, M., Alterman, V., Alonso, A., Robinson, S.: Leveraging age diversity for organizational performance: An intellectual capital perspective. Journal of Applied 32 Psychology106(1), 71 (2021)

    work page 2021

  76. [76]

    Nature559(7714), 396–399 (2018)

    Liu, L., Wang, Y., Sinatra, R., Giles, C.L., Song, C., Wang, D.: Hot streaks in artistic, cultural, and scientific careers. Nature559(7714), 396–399 (2018)

    work page 2018

  77. [77]

    Nature Communications10(1), 4331 (2019)

    Wang, Y., Jones, B.F., Wang, D.: Early-career setback and future career impact. Nature Communications10(1), 4331 (2019)

    work page 2019

  78. [78]

    Nature575(7781), 190–194 (2019)

    Yin, Y., Wang, Y., Evans, J.A., Wang, D.: Quantifying the dynamics of failure across science, startups and security. Nature575(7781), 190–194 (2019)

    work page 2019

  79. [79]

    Nature Communications12(1), 5392 (2021)

    Liu, L., Dehmamy, N., Chown, J., Giles, C.L., Wang, D.: Understanding the onset of hot streaks across artistic, cultural, and scientific careers. Nature Communications12(1), 5392 (2021)

    work page 2021

  80. [80]

    Scientometrics128(3), 1801–1823 (2023)

    Wang, Y., Li, N., Zhang, B., Huang, Q., Wu, J., Wang, Y.: The effect of structural holes on producing novel and disruptive research in physics. Scientometrics128(3), 1801–1823 (2023)

    work page 2023

Showing first 80 references.