Lukasz Szymula

Marek Kwiek, Lukasz Szymula

In this study, the global scientific workforce is explored through large-scale, generational, cross-sectional, and longitudinal approaches. We examine 4.3 million nonoccasional scientists from 38 OECD countries publishing in 1990-2021. Our interest is in the changing distribution of young male and female scientists over time across 16 STEMM (science, technology, engineering, mathematics, medicine) disciplines. We unpack the details of the changing scientific workforce using age groups. Some disciplines are already numerically dominated by women, and the change is fast in some and slow in other disciplines. In one-third of disciplines, there are already more youngest female than male scientists. Across all disciplines combined, the majority of women are young women. And more than half of women scientists (55.02%) are located in medicine. The usefulness of global bibliometric data sources in analyzing the scientific workforce along gender, age, discipline, and time is tested. Traditional aggregated data about scientists in general hide a nuanced picture of the changing gender dynamics within and across disciplines and age groups. The limitations of bibliometric datasets are explored, and global studies are compared with national-level studies. The methodological choices and their implications are shown, and new opportunities for how to study scientists globally are discussed.

Marek Kwiek, Lukasz Szymula

The present study focuses on persistence in research productivity over the course of an individual's entire scientific career. We track 'late-career' scientists - scientists with at least 25 years of publishing experience (N=320,564) - in 16 STEMM (science, technology, engineering, mathematics, and medicine) and social science disciplines from 38 OECD countries for up to five decades. Our OECD sample includes 79.42% of late-career scientists globally. We examine the details of their mobility patterns as early-career, mid-career, and late-career scientists between decile-based productivity classes, from the bottom 10% to top 10% of the productivity distribution. Methodologically, we turn a large-scale bibliometric dataset (Scopus raw data) into a comprehensive, longitudinal data source for research on careers in science. The global science system is highly immobile: half of global top performers continue their careers as top performers and one-third of global bottom performers as bottom performers. Jumpers-Up and Droppers-Down are extremely rare in science. The chances of moving radically up or down in productivity classes are marginal (1% or less). Our regression analyses show that productivity classes are highly path dependent: there is a single most important predictor of being a top performer, which is being a top performer at an earlier career stage.

Marek Kwiek, Lukasz Szymula

In this research, we quantify an inflow of women into science in the past three decades. Structured Big Data allow us to estimate the contribution of women scientists to the growth of science by disciplines (N = STEMM 14 disciplines) and over time (1990-2023). A monolithic segment of STEMM science emerges from this research as divided between the disciplines in which the growth was powerfully driven by women - and the disciplines in which the role of women was marginal. There are four disciplines in which 50% of currently publishing scientists are women; and five disciplines in which more than 50% of currently young scientists are women. But there is also a cluster of four highly mathematized disciplines (MATH, COMP, PHYS, and ENG) in which the growth of science is only marginally driven by women. Digital traces left by scientists in their publications indexed in global datasets open two new dimensions in large-scale academic profession studies: time and gender. The growth of science in Europe was accompanied by growth in the number of women scientists, but with powerful cross-disciplinary and cross-generational differentiations. We examined the share of women scientists coming from ten different age cohorts for 32 European and four comparator countries (the USA, Canada, Australia, and Japan). Our study sample was N = 1,740,985 scientists (including 39.40% women scientists). Three critical methodological challenges of using structured Big Data of the bibliometric type were discussed: gender determination, academic age determination, and discipline determination.