Age and Scientific Performance

Cole, Stephen

doi:10.1086/226868

Cited by 312 publications

(213 citation statements)

References 7 publications

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“…These models have included factors like cognitive decline with age, career age, finite supplies of human capital, knowledge advantages conferred by recent education, as well as skill deficits among the young, among others, and have been supported by the observation that individual productivity curves feature both long-and medium-term fluctuations [12] and are not well described by even fourth-degree polynomial models [9]. Indeed, every study we found to date proposes or confirms a * samuel.way@colorado.edu † allison.morgan@colorado.edu ‡ aaron.clauset@colorado.edu § daniel.larremore@colorado.edu "rise and decline," "curvilinear," or "peak and tapering" productivity trajectory, regardless of whether researchers are binned by chronological age [5][6][7][8][10][11][12][13], career age [9,10], or (only for young researchers) years since first publication [14]. The pattern may even extend to mentorship, supported by a finding that the protégés of early-career mathematicians tended to mentor more students, themselves, than protégés trained by those same faculty late in their careers [15].…”

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confidence: 99%

The misleading narrative of the canonical faculty productivity trajectory

Way

Morgan

Clauset

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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A scientist may publish tens or hundreds of papers over a career, but these contributions are not evenly spaced in time. Sixty years of studies on career productivity patterns in a variety of fields suggest an intuitive and universal pattern: productivity tends to rise rapidly to an early peak and then gradually declines. Here, we test the universality of this conventional narrative by analyzing the structures of individual faculty productivity time series, constructed from over 200,000 publications and matched with hiring data for 2453 tenure-track faculty in all 205 Ph.D-granting computer science departments in the U.S. and Canada. Unlike prior studies, which considered only some faculty or some institutions, or lacked common career reference points, here we combine a large bibliographic dataset with comprehensive information on career transitions that covers an entire field of study. We show that the conventional narrative confidently describes only one fifth of faculty, regardless of department prestige or researcher gender, and the remaining four fifths of faculty exhibit a rich diversity of productivity patterns. To explain this diversity, we introduce a simple model of productivity trajectories, and explore correlations between its parameters and researcher covariates, showing that departmental prestige predicts overall individual productivity and the timing of the transition from first-to last-author publications. These results demonstrate the unpredictability of productivity over time, and open the door for new efforts to understand how environmental and individual factors shape scientific productivity. INTRODUCTIONScholarly publications serve as the primary mode of communication through which scientific knowledge is developed, discussed, and disseminated. The amount that an individual researcher contributes to this dialoguetheir scholarly productivity-thus serves as an important measure of the rate at which they contribute units of knowledge to the field, and this measure is known to influence the placement of graduates into faculty jobs [1], the likelihood of being granted tenure [2,3], and the ability to secure funding for future research [4].The trajectory of productivity over the course of a researcher's lifetime has been studied for at least 60 years, with the common observation being that a researcher's productivity rises rapidly to a peak and then slowly declines [5][6][7][8][9], which has inspired the construction of mechanistic models with a similar profile [7,[9][10][11][12]. These models have included factors like cognitive decline with age, career age, finite supplies of human capital, knowledge advantages conferred by recent education, as well as skill deficits among the young, among others, and have been supported by the observation that individual productivity curves feature both long-and medium-term fluctuations [12] and are not well described by even fourth-degree polynomial models [9]. Indeed, every study we found to date proposes or confirms a * samuel.way@colorado.edu † allison.morg...

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mentioning

confidence: 99%

The misleading narrative of the canonical faculty productivity trajectory

Way

Morgan

Clauset

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Among others, Zuckerman and Merton (1972), Cole (1979), Diamond (1984), Colander (1989), Goodwin and Sauer (1995), Oster and Hamermesh (1998) or Breschi et al (2004) can be mentioned. While some of those papers confirm the validity of Beard's law, some provide opposite results.…”

Section: The Age -Productivity Relationship: Theoretical Backgroundmentioning

confidence: 99%

“…He found that more discoveries are made by young scientists than by old ones. His methodology was criticized (among others) by Cole (1979 Lehman's results are not evidence (but also they are not contradictory) of the conclusion that age has causal influence on scientific productivity.…”

Section: The Age -Productivity Relationship: Theoretical Backgroundmentioning

confidence: 99%

“…However, if the rewarding system in the academia does not reward publication activity (or favors higher positions instead of high publication activity), it can motivate scientists to publish only at the start of their career to get to higher positions. Most people will not continue an activity as arduous as scientific research unless they are rewarded for it (Cole, 1979).…”

Section: The Age -Productivity Relationship: Theoretical Backgroundmentioning

confidence: 99%

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Age, academic career and scientific performance of Czech economists

Wroblowský

2010

ER-CEREI

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“…Lehman (1953) first proposed a scientist's age impacts research productivity. Later research by Allison and Steward (1974), Bayer and Dutton (1977), Cole (1979), Allison et al (1982), and Diamond (1986) provides empirical evidence of the life cycle age of scientists' connection to research productivity. Levin and Stephan (1991) sought to overcome limitations in the prior research by providing a comprehensive, longitudinal study to examine the effects of aging on different groups of scientists.…”

Section: Introductionmentioning

confidence: 99%

Operations research methodology life cycle trend phases as recorded in journal articles

Schniederjans

2009

Journal of the Operational Research Society

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Age and Scientific Performance

Cited by 312 publications

References 7 publications

The misleading narrative of the canonical faculty productivity trajectory

The misleading narrative of the canonical faculty productivity trajectory

Age, academic career and scientific performance of Czech economists

Operations research methodology life cycle trend phases as recorded in journal articles

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