Presentation given at the Atlanta Conference on Science and Innovation Policy

1. Towards a multidimensional valuation
model of scientists
http://nrobinsongarcia.com
@nrobinsongarcia

2. Critiques to individual assessment
– DORA – Impact Factor
– Leiden Manifesto – Misuse of indicators
– Metric Tide – Indicators are not adequate
Threats from current research evaluation
1. Evaluation schemes rely heavily on journal publication and citation-based
indicators
2. Invisible profiles are degraded despite growing importance
3. Changes in the scholarly system are ignored
Motivation

3. Critiques to individual assessment
– DORA – Impact Factor
– Leiden Manifesto – Misuse of indicators
– Metric Tide – Individual assessment
Threats from current research evaluation
1. Evaluation schemes rely heavily on journal publication and citation-based
indicators
2. Invisible profiles are degraded despite growing importance
3. Changes in the scholarly system are ignored
Motivation

4. Effects of current evaluation schemes
• Effects on the scientific workforce
– Task reduction (de Rijcke et al, 2016)
– Homogeneity of profiles (Milojević et al, 2018)
– Mistrust and conservatism (Abramo et al, 2015)
• Effects on knowledge production
– Quality of research and transparency (Moher et al 2018)
– Research integrity (Naudet et al 2018)
– Short-sighted research agenda (PoP culture)

5. Conceptual framework
• What is expected of a scientist?
Evaluative dimensions of performativity
• What is their potential to achieve such
expectations?
Constraints and confounding effects

6. Conceptual framework
Evaluative
dimensions
External factors
Personal
features
• Nationality
• Gender
• Language
• ….
• Performance
• Expectations
• ….
• Work environment
• Institutional logics
• National policies
• …
Opportunity to
perform

7. Evaluative dimensions
• Scientific engagement
• Community career (Laudel &
Gläser, 2008)
• Overall scientific production
• Social engagement
• Outreach
• Participatory science
• Capacity building
• Resources
• Human capital
• Trajectory
• Past experience
i.e., international, non-academic
• Open practices
• Transparency
• Reproducibility
• Participatory

8. Research questions
1. Are there different research profiles?
2. Is there a correspondence with career stage?
3. Do current research evaluation schemes favor certain
profiles?

9. Roadmap
1. Proof-of-concept
• Desktop research
• Interviews
• CV analysis
• Exploratory
analyses
2. Descriptive phase
• Operationalization
• Profiling of
scientists
3. Analytic phase
• Career
trajectories
• Analysis by
gender,
nationality, field
• Contextualization

10. Roadmap
1. Proof-of-concept
• Desktop research
• Interviews
• CV analysis
• Exploratory
analyses
2. Descriptive phase
• Operationalization
• Profiling of
scientists
3. Analytic phase
• Career
trajectories
• Analysis by
gender,
nationality, field
• Contextualization

11. Research design – Multiple case study
• 6 Research groups: 228 scientists
• 2 universities: Technical vs. Multidisciplinary
• 6 research fields: Physics, Biomedical Sciences and Social
Sciences
Multidisciplinary Univ Technical Univ
Biomedical Sciences 15 19
Social Sciences 9 61
Physics 118 6

12. Research design – Data sources
• Web of Science and Google Scholar – Research outputs
• CV and personal website – Trajectory, model validation
• Social media activity – Outreach
• Interviews – Motivations, model validation

13. Exploratory analysis
VARIABLES
Scientific
engagement
Share of co-authored
papers
Social
engagement
Share of papers co-
authored with
industry
Capacity
building
Number of first-year
authors, last position,
continued publishing
Trajectory Years since first
publication
Open practices
Share of papers
available in Open
Access

14. Exploratory analysis
ALL BIBLIOMETRIC!! VARIABLES
Scientific
engagement
Share of co-authored
papers
Social
engagement
Share of papers co-
authored with
industry
Capacity
building
Number of first-year
authors, last position,
continued publishing
Trajectory Years since first
publication
Open practices
Share of papers
available in Open
Access

15. Exploratory analysis
ARCHETYPAL ANALYSIS
• Statistical data representation technique to characterize
multivariate data sets (Cutler & Braiman, 1994)
• First used in scientometrics in 2013 (Seiler & Wohlrabe, 2013)
• It defines archetypes of individuals based on extreme values of
one or more variables
• Individuals are then characterized as pure or mixtures of
archetypes

16. Preliminary results – All scientists
• 228 scientists
• 3 distinct fields
• Physics
• Social Sciences
• Biomedicine

17. Preliminary results – All scientists
• Find most suitable number
of archetypes
• Iteration process (4) trying
to up to 10 archetypes
• Check Residual Sum of
Squares (RSS)
• Apply elbow rule

18. Preliminary results – All scientists
• Arc. 1 High industry
collaboration
• Arc. 2 High age & pupils
• Arc. 3 High collaboration
& OA
• Arc. 4 Middle age &
middle collab. & middle
OA
Collaboration Industry Pupils Age Open Access

19. Preliminary results – Social Sciences
• Arc. 1 Middle age &
middle OA
• Arc. 2 High age & pupils
• Arc. 3 High industry &
collaboration
• Arc. 4 High collab & high
OA
Collaboration Industry Pupils Age Open Access

20. Preliminary results – Physics
• Arc. 1 High OA & middle
collab & middle industry
• Arc. 2 Middle collab &
high industry & middle
pupils
• Arc. 3 Middle OA & middle
age
• Arc. 4 High age & pupils &
industry
Collaboration Industry Pupils Age Open Access

21. Preliminary results – Biomedicine
• Arc. 1 High collab & high
industry
• Arc. 2 Middle industry &
middle age
• Arc. 3 High OA & high
collab
• Arc. 4 High age & high
pupils
Collaboration Industry Pupils Age Open Access

22. Preliminary conclusions
• Need for constructive discussions on limitations of current research
assessment schemes of individual
• Expectations from scientists
• Modelling of research career and trajectories
• Stop isolating performance
• Development of balanced valuation models
• What do we value and how can it be observed
• Ambiguity vs. reductionism

23. Preliminary conclusions
Archetype 1 Archetype 2 Archetype 3 Archetype 4
Soc Sci Traj Cap Op Soc Sci Traj Cap Op Soc Sci Traj Cap Op Soc Sci Traj Cap Op
Social Sciences
Physics
Biomedicine
Overall
Industry-oriented Mentors (Open) collaborators The middle class
• Despite being very different areas and the limitations of the indicators
identified we observe some consistency in the profiles
• OA differences especially for Biomedicine in archetype 1
• Some profiles (e.g. 2) are ridden mostly by career stage

24. Thank you!!
http://nrobinsongarcia.com
@nrobinsongarcia

25. References
Abramo, G., D’Angelo, C. A., & Rosati, F. (2015). The determinants of academic career advancement: Evidence from Italy.
Science and Public Policy, 42(6), 761–774. https://doi.org/10.1093/scipol/scu086
Cutler, A., & Breiman, L. (1994). Archetypal Analysis. Technometrics, 36(4), 338–347.
https://doi.org/10.1080/00401706.1994.10485840
Laudel, G., & Gläser, J. (2008). From apprentice to colleague: The metamorphosis of Early Career Researchers. Higher
Education, 55(3), 387–406. https://doi.org/10.1007/s10734-007-9063-7
Milojević, S., Radicchi, F., & Walsh, J. P. (2018). Changing demographics of scientific careers: The rise of the temporary
workforce. Proceedings of the National Academy of Sciences, 115(50), 12616–12623.
https://doi.org/10.1073/pnas.1800478115
Moher, D., Naudet, F., Cristea, I. A., Miedema, F., Ioannidis, J. P. A., & Goodman, S. N. (2018). Assessing scientists for hiring,
promotion, and tenure. PLOS Biology, 16(3), e2004089. https://doi.org/10.1371/journal.pbio.2004089
Naudet, F., Ioannidis, J. P. A., Miedema, F., Cristea, I. A., Goodman, S. N., & Moher, D. (2018, June 4). Six principles for
assessing scientists for hiring, promotion, and tenure. Retrieved 7 June 2018, from Impact of Social Sciences website:
http://blogs.lse.ac.uk/impactofsocialsciences/2018/06/04/six-principles-for-assessing-scientists-for-hiring-promotion-and-
tenure/
Rijcke, S. de, Wouters, P. F., Rushforth, A. D., Franssen, T. P., & Hammarfelt, B. (2016). Evaluation practices and effects of
indicator use—A literature review. Research Evaluation, 25(2), 161–169. https://doi.org/10.1093/reseval/rvv038
Seiler, C., & Wohlrabe, K. (2013). Archetypal scientists. Journal of Informetrics, 7(2), 345–356.
https://doi.org/10.1016/j.joi.2012.11.013