The productivity of top researchers: A semi-nonparametric approach
Research productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In part...
- Autores:
-
Cortés, Lina M.
Perote, Javier
Mora-Valencia, Andrés
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/8181
- Acceso en línea:
- http://hdl.handle.net/10784/8181
- Palabra clave:
- Research evaluation
Research productivity
Heavy tail distributions
Semi- nonparametric modeling
- Rights
- License
- Acceso abierto
| Summary: | Research productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In particular, distributions such as the lognormal distribution seem to systematically underestimate the productivity of the top researchers. In this article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests the lognormal and captures the heavy tail of the productivity distribution through the introduction of new parameters linked to high-order moments. To compare the results, we use research performance data on 140,971 researchers who have produced 253,634 publications in 18 fields of knowledge (O’Boyle and Aguinis, 2012) and show how the log-SNP distribution provides more accurate measures of the performance of the top researchers in their respective fields of knowledge. |
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