The determinants and impact of research grants: The case of Brazilian productivity scholarships

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics Pub Date : 2024-07-24 DOI:10.1016/j.joi.2024.101563

Marcelo Perlin, Denis Borenstein, Takeyoshi Imasato, Marcos Reichert

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引用次数: 0

Abstract

Research Productivity Grant (PQ) is a governmental research award maintained by CPNq, the Brazilian Council of Research, and designed as a funding program to support scientific studies in all fields of science. Using a compilation of data from the Lattes platform, we study the individual CVs of more than 133000 researchers between 2005 and 2022 to examine PQ's selection criteria and impact on research productivity over time. First, a machine learning model can accurately predict who receives the financial support. This suggests that some parts of the evaluation process can be automated based on Lattes. Moreover, the main factors that impact the likelihood of a researcher receiving an entry-level PQ are the number of supervisions and papers published. These factors are consistent across different fields of science. Additionally, we found a significant and positive impact from receiving the award in key academic research output. After receiving a CNPq productivity award, researchers tend to increase the number of citations of papers and publications.

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研究补助金的决定因素和影响：巴西生产力奖学金案例

研究生产率补助金（PQ）是由巴西研究理事会 CPNq 设立的一项政府研究奖励，旨在为所有科学领域的科学研究提供资助。我们利用 Lattes 平台的数据汇编，研究了 2005 年至 2022 年间 133000 多名研究人员的个人简历，以考察 PQ 的遴选标准以及随着时间推移对研究生产率的影响。首先，机器学习模型可以准确预测谁能获得资助。这表明，评估过程的某些部分可以基于 Lattes 实现自动化。此外，影响研究人员获得入门级 PQ 的可能性的主要因素是指导次数和发表论文的数量。这些因素在不同的科学领域都是一致的。此外，我们还发现，获得该奖项对关键学术研究产出有显著的积极影响。获得 CNPq 生产力奖后，研究人员往往会增加论文和出版物的引用次数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Informetrics Social Sciences-Library and Information Sciences

CiteScore

6.40

自引率

16.20%

发文量

期刊介绍： Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.