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From theory to practice: Operationalization of the GTEC framework

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Presentation held at the STI 2018 Conference held in Leiden, September 12-14, 2018.

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From theory to practice: Operationalization of the GTEC framework

  1. 1. FROM THEORY TO PRACTICE OPERATIONALIZATION OF THE GTEC FRAMEWORK Nicolas Robinson-Garcia, Eric van Holm, Julia Melkers and Eric Welch
  2. 2. THE CHALLENGES OF A GLOBALIZED SCIENCE SYSTEM 2 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. 3. A NATIONAL PERSPECTIVE ON GLOBALIZATION 3
  4. 4. A NATIONAL PERSPECTIVE ON GLOBALIZATION 4 •Binary concept Mobile vs. not mobile | Foreign vs. National •Unidimensional Personal trait •Decontextualized Detached from external factors
  5. 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  6. 6. 6 DATA CHALLENGES National surveys Bibliometric sources Institutional sources • Purpose-oriented • Outdated • Partial and incomplete • Decontextualized • Geographically constrained • …
  7. 7. Does an expanded view of globalness produce dissimilar results than traditional approaches on mobility or foreigness? • Data-driven definition? • Ignored factors affecting interpretation? • … 7 BUT…
  8. 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. 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. 10 Citizenship status by mobility type and race DIVERSITY TRAITS & EXPERIENCES
  11. 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
  12. 12. 12 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. 13 Comparison of means Satisfaction with salary •Significance varies based on definition of foreigness •Differences suggest different layers of foreigness
  14. 14. 14 NBR analysis Average publications per year
  15. 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 15 IDEAS FOR DISCUSSION
  16. 16. THANK YOU! From theory to practice: Operationalization of the GTEC framework Nicolas Robinson-Garcia, Eric van Holm, Julia Melkers and Eric Welch
  17. 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) 17

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