This document discusses operationalizing the Globalization of Talent and the Economy in Science (GTEC) framework using survey and bibliometric data. The GTEC framework considers globalness as multidimensional rather than a single trait like foreignness. It examines how globalness affects scientists through traits, experiences, and external factors. The document applies the GTEC framework to US survey data on over 4,000 scientists, linking it to bibliometric and institutional data through author name disambiguation and a mobility taxonomy. It finds heterogeneity among scientists based on different definitions of globalness. Experiments show how results on foreign-born scientists' productivity and satisfaction vary based on the globalness definition used. The GTEC framework thus allows for more nuanced analysis

1. FROM THEORY TO PRACTICE
OPERATIONALIZATION OF THE GTEC FRAMEWORK
Nicolas Robinson-Garcia, Eric van Holm, Julia Melkers and Eric Welch

2. THE CHALLENGES OF A GLOBALIZED SCIENCE SYSTEM
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Participate in global economyBuild nation’s infrastructure
Tackle opportunities accross
national boundaries
Respond to localized societal
problems
Convergence of practices
Retain national competitive
advantage
Nerad, 2010

3. A NATIONAL PERSPECTIVE ON GLOBALIZATION
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4. A NATIONAL PERSPECTIVE ON GLOBALIZATION
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•Binary concept
Mobile vs. not mobile | Foreign vs. National
•Unidimensional
Personal trait
•Decontextualized
Detached from external factors

5. 5
GTEC FRAMEWORK
 Beyond foreignness
 Dimensions of globalness
 Characteristics or effects of
globalness
 Interrelated dimensions
More info at Welch et al., 2018

6. 6
DATA CHALLENGES
National surveys
Bibliometric sources
Institutional sources
• Purpose-oriented
• Outdated
• Partial and incomplete
• Decontextualized
• Geographically constrained
• …

7. Does an expanded view of globalness produce
dissimilar results than traditional approaches on
mobility or foreigness?
• Data-driven definition?
• Ignored factors affecting interpretation?
• …
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BUT…

8. 8
Layers of globalness - DIVERSITY
• Globalness as a trait/experience variable
• Alternative variables and heterogeneity
• Combining different definitions
Consequences for studies on foreign-born faculty
• Foreign-born faculty are more productive than US born
(Corley & Sabharwal, 2007)
• Foreign-born faculty are less satisfied with their salary than
US born (Sabharwal, 2011)

9. 9
The GTEC
Framework
in practice
An attempt to illustrate the
global scientific workforce
through the GTEC lenses
• US Survey data
• NETWISE I 1597 respondents
• NETWISE II 4195 respondents
• Bibliometric data
• Web of Science
• Institutional data
• Carnegie Classification
• Author name disambiguation
algorithm
• Mobility taxonomy
• Ethnicity algorithm

10. 10
Citizenship status by
mobility type and race
DIVERSITY TRAITS & EXPERIENCES

11. 11
• Different perspectives on globalness from
different groupings
• Combining perspectives leads to great
degree of heterogeneity
• Only dummy variables are being
considered here
DIVERSITY TRAITS & EXPERIENCES

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Consequences for studies on foreign-born faculty:
An experiment
1. Foreign-born faculty are less satisfied with their
salary than US born (Sabharwal, 2011)
▪ Comparison of means
▪ Explanatory variables: citizenship, PhD training, mobility
and US parents
2. Foreign-born faculty are more productive than US
born (Corley & Sabharwal, 2007)
▪ Negative Binomial Regression Analysis
▪ NET II only (N = 2,713)
▪ Explanatory variables: citizenship, PhD training mobility

13. 13
Comparison of means Satisfaction with salary
•Significance varies based on
definition of foreigness
•Differences suggest different
layers of foreigness

14. 14
NBR analysis Average publications per year

15. •GTEC framework sets the ground for more nuanced
analyses of the effects of globalization in science
•Need for consistent data but also to connect and
enrich with third-party data
•Common framework and understanding despite the
approach or perspective to allow benchmarking and
comparisons
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IDEAS FOR DISCUSSION

16. THANK YOU!
From theory to practice:
Operationalization of the GTEC framework
Nicolas Robinson-Garcia, Eric van Holm, Julia Melkers and Eric Welch

17. •Author name disambiguation algorithm
• Scoring-rules algorithm Caron & van Eck
(2014)
• Matching between pub. clusters and survey respondents
•Mobility taxonomy
• 2008-2015 dataset Robinson-Garcia, et al.
(2018)
• Mobility types: migrants, travelers and non-migrants
FINAL DATA SET: 4,063 respondents
(Net I= 1,350; Net II= 2,713)
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