Top Research Performance Over Three Decades: A Multidimensional Micro-Data Approach
<|reference_start|>Top Research Performance Over Three Decades: A Multidimensional Micro-Data Approach: In this research, the contributions of a highly productive minority of scientists to the national Polish research output over the past three decades (1992-2021) is explored. In almost all previous research, the approaches to high research productivity are missing the time component. Cross-sectional studies were not complemented by longitudinal studies: Scientists comprising the classes of top performers have not been tracked over time. Three classes of top performers (the upper 1%, 5%, and 10%) are examined, and a surprising temporal stability of productivity patterns is found. The 1/10 and 10/50 rules consistently apply across the three decades: The upper 1% of scientists, on average, account for 10% of the national output, and the upper 10% account for almost 50% of total output, with significant disciplinary variations. The Relative Presence Index (RPI) we constructed shows that men are overrepresented and women underrepresented in all top performers classes. Top performers are studied longitudinally through their detailed publishing histories, with micro-data coming from the raw Scopus dataset. Econometric models identify the three most important predictors that change the odds ratio estimates of membership in the top performance classes: gender, academic age, and research collaboration. The downward trend in fixed effects over successive six-year periods indicates increasing competition in academia. A large population of all internationally visible Polish scientists (N=152,043) with their 587,558 articles is studied.<|reference_end|>
@article{kwiek2024top,
    title={Top Research Performance Over Three Decades: A Multidimensional
  Micro-Data Approach},
    author={Marek Kwiek, Wojciech Roszka},
    journal={arXiv preprint arXiv:2407.04199},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04199},
    primaryClass={cs.DL}
}