Differences in Collaboration Patterns across Discipline, Career Stage, and Gender

Zeng, Xiao Han T.; Duch, Jordi; Sales‐Pardo, Marta; Moreira, João; Radicchi, Filippo; Ribeiro, Haroldo V.; Woodruff, Teresa K.; Amaral, Luı́s A. Nunes

doi:10.1371/journal.pbio.1002573

Cited by 111 publications

(105 citation statements)

References 40 publications

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“…Gender has been shown to correlate with differences in productivity across fields [37][38][39], but these relationships are complicated by prestige [26] and have also changed over time [1]. Other work has uncovered differences in collaboration patterns between subfields [40], as well as productivity differences that depend on both student and advisor genders [41]. Here, we found that men and women follow the canonical productivity narrative at equal rates.…”

Section: Discussionsupporting

confidence: 52%

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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Section: Discussionsupporting

confidence: 52%

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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“…For example, they may be more collaborative (Zeng et al. ), or more likely to promote junior colleagues' first‐authored publications (Pezzoni et al. ), which can be penalized where reviewers are looking for single‐ and first‐author contributions by faculty (Sarsons ).…”

Section: Discussionmentioning

confidence: 99%

Careers in ecology: a fine‐scale investigation of national data from the U.S. Survey of Doctorate Recipients

Hampton

Labou

2017

Ecosphere

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Abstract. For several decades, economists have been warning the academic community that graduate training has been too tightly focused on careers in higher education, using the apprenticeship model in which students are trained to become tenure-track faculty at research-focused institutions. These jobs are simply not growing at the same rate as graduate admissions. In biomedical research, the mismatch in supply and demand has now been widely recognized. Other disciplines have begun these discussions, but for smaller fields, employment trends are more difficult to identify because they are subsumed in aggregated national statistics. For non-biomedical biological fields in particular, such as ecology, using biology statistics may be inappropriate since trends within the field may be obscured by the strong signal from biomedical disciplines. Here, we use the 2013 Survey of Doctorate Recipients (SDR) to investigate career paths for ecology Ph.D. recipients in the United States, and present the first fine-scale national profile of careers in ecology. Our results demonstrate that while involuntary unemployment is low for ecology Ph.D. recipients (3.3%) and job satisfaction is high, the assumptions of the prevailing apprenticeship model are inappropriate: Less than 20% of employed recent ecology Ph.D. graduates are in tenure-track positions at a Ph.D.-granting university. Accordingly, proactive steps could be taken to create more realistic expectations about graduate training and preparation for diverse careers. Further, the SDR data provide demographic profiles for ecology. Ethnic diversity has remained low in ecology (7.5% non-Caucasian for Ph.D. recipients since 2000). Gender balance in career-track positions appears to have improved by multiple metrics. However, women are overrepresented in non-tenure-track academic positions, where access to resources that support professional advancement may be limited relative to tenure-track jobs, and salary disparities appear for women in private academic institutions. Thus, while there is much good news in these data, we suggest that ecology as a field would benefit from (1) a broad analysis of the training required to make Ph.D.s best prepared for jobs outside of the research-oriented tenure track and (2) continued attention to increasing diversity and equity.

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“…Women have fewer publications (35–37) and collaborators (38) and less funding (39), and they are penalized in hiring decisions when compared with equally qualified men (40). The causes of these gaps are still unclear.…”

Section: Career Dynamicsmentioning

confidence: 99%

“…The causes of these gaps are still unclear. Intrinsic differences in productivity rates and career length can explain the differences in collaboration patterns (38) and hiring rates (35) between male and female scientists. On the other hand, experimental evidence shows that biases against women occur at very early career stages.…”

Section: Career Dynamicsmentioning

confidence: 99%

Science of science

Fortunato

Bergstrom

Börner

et al. 2018

Science

Self Cite

896

681

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BACKGROUND The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems. ADVANCES Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact. OUTLOOK SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as sciento-metrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for un...

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Differences in Collaboration Patterns across Discipline, Career Stage, and Gender

Cited by 111 publications

References 40 publications

The misleading narrative of the canonical faculty productivity trajectory

The misleading narrative of the canonical faculty productivity trajectory

Careers in ecology: a fine‐scale investigation of national data from the U.S. Survey of Doctorate Recipients

Science of science

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