The Research Focus of Nations: Economic vs. Altruistic Motivations

Klavans, Richard; Boyack, Kevin W.

doi:10.1371/journal.pone.0169383

Cited by 17 publications

(13 citation statements)

References 44 publications

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“…The present work did not assume any clusters of scientific fields a priori, but identified the clusters based on the data. This is different from studies which assume a priori a particular clustering of disciplines [30], which may explain why those studies found that national wealth was not associated with tradeoffs between altruistic and economic motives. While finer methods for clustering areas of research based on publication data have been performed [31,32], the work presented in this manuscript focused on a more course approach to extract more robust relationships and their dynamics over time.…”

Section: Plos Onecontrasting

confidence: 67%

A network analysis of research productivity by country, discipline, and wealth

et al. 2020

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Introduction Research productivity has been linked to a country's intellectual and economic wealth. Further analysis is needed to assess the association between the distribution of research across disciplines and the economic status of countries. Methods By using 55 years of data, spanning 1962 to 2017, of Elsevier publications across a large set of research disciplines and countries globally, this manuscript explores the relationship and evolution of relative research productivity across different disciplines through a network analysis. It also explores the associations of those with economic productivity categories, as measured by the World Bank economic classification. Additional analysis of discipline similarities is possible by exploring the crosscountry evolution of those disciplines. Results Results show similarities in the relative importance of research disciplines among most high-income countries, with larger idiosyncrasies appearing among the remaining countries. This group of high-income countries shows similarities in the dynamics of the relative distribution of research productivity over time, forming a stable research productivity cluster. Lower income countries form smaller, more independent and evolving clusters, and differ significantly from each other and from higher income countries in the relative importance of their research emphases. Country-based similarities in research productivity profiles also appear to be influenced by geographical proximity. Conclusions This new form of analyses of research productivity, and its relation to economic status, reveals novel insights to the dynamics of the economic and research structure of countries. This allows for a deeper understanding of the role a country's research structure may play in

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Section: Plos Onecontrasting

confidence: 67%

A network analysis of research productivity by country, discipline, and wealth

et al. 2020

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“…The demand for science to address social and health needs is prevalent, and can be represented by metrics such as disease burden (Atanassova, 2016). Some science, particularly that invested in by industry, responds to economic motives (Klavans & Boyack, 2017a). These diverse demands are important, and they (and their advocates) obviously play a role in priority setting within governments, agencies, and other funding bodies.…”

Section: Introductionmentioning

confidence: 99%

Research portfolio analysis and topic prominence

Klavans

Boyack

2017

Journal of Informetrics

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Stakeholders in the science system need to decide where to place their bets. Example questions include: Which areas of research should get more funding? Who should we hire? Which projects should we abandon and which new projects should we start? Making informed choices requires knowledge about these research options. Unfortunately, to date research portfolio options have not been defined in a consistent, transparent and relevant manner. Furthermore, we don't know how to define demand for these options. In this article, we address the issues of consistency, transparency, relevance and demand by using a model of science consisting of 91,726 topics (or research options) that contain over 58 million documents. We present a new indicator of topic prominence -a measure of visibility, momentum and, ultimately, demand. We assign over $203 billion of project-level funding data from STAR METRICS ® to individual topics in science, and show that the indicator of topic prominence, explains over one-third of the variance in current (or future) funding by topic. We also show that highly prominent topics receive far more funding per researcher than topics that are not prominent. Implications of these results for research planning and portfolio analysis by institutions and researchers are emphasized.

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“…The research communities in the DC5 model had been previously aggregated to 114 disciplines (known as DC2 because it has~10 2 clusters, while DC5 has~10 5 clusters) and then further to nine high level fields. The process by which this was done is described in detail in Klavans & Boyack [29]. While RCs in the DC5 model were directly mapped to disciplines and fields, we assigned RCs in the STS5 models to DC2 disciplines and fields using common papers from 2008-2014.…”

Section: Test #2: Precision and Recall By Field (Dc5-2014 And Sts5-2014)mentioning

confidence: 99%

A novel approach to predicting exceptional growth in research

Klavans¹,

Boyack²,

Murdick³

2020

PLoS ONE

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The prediction of exceptional or surprising growth in research is an issue with deep roots and few practical solutions. In this study, we develop and validate a novel approach to forecasting growth in highly specific research communities. Each research community is represented by a cluster of papers. Multiple indicators were tested, and a composite indicator was created that predicts which research communities will experience exceptional growth over the next three years. The accuracy of this predictor was tested using hundreds of thousands of community-level forecasts and was found to exceed the performance benchmarks established in Intelligence Advanced Research Projects Activity's (IARPA) Foresight Using Scientific Exposition (FUSE) program in six of nine major fields in science. Furthermore, 10 of 11 disciplines within the Computing Technologies field met the benchmarks. Specific detailed forecast examples are given and evaluated, and a critical evaluation of the forecasting approach is also provided.

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The Research Focus of Nations: Economic vs. Altruistic Motivations

Cited by 17 publications

References 44 publications

A network analysis of research productivity by country, discipline, and wealth

A network analysis of research productivity by country, discipline, and wealth

Research portfolio analysis and topic prominence

A novel approach to predicting exceptional growth in research

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