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Task specialization across research careers

Slides of a presentation given on February 12, 2021 at the Complexity Science Hub Vienna. Paper: https://elifesciences.org/articles/60586

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Task specialization across research careers

  1. 1. TASK SPECIALIZATION ACROSS RESEARCH CAREERS N. Robinson-Garcia, R. Costas, C.R. Sugimoto, V. Larivière and G.F. Nane
  2. 2. 2 UNVEILING THE ECOSYSTEM OF SCIENCE: A CONTEXTUAL PERSPECTIVE ON THE MANY ROLES OF SCIENTISTS FRAMEWORK OF THE STUDY
  3. 3. 3 UNVEILING THE ECOSYSTEM OF SCIENCE: A CONTEXTUAL PERSPECTIVE ON THE MANY ROLES OF SCIENTISTS FRAMEWORK OF THE STUDY https://elifesciences.org/articles/60586 https://zenodo.org/record/3891055
  4. 4. 4 1. Can we identify diversity of profiles in science? • How can we identify it? • Can diversity in science be described in a systematic way? 2. How does research evaluation affect diversity? • Are there observable differences on research trajectories by type of profile? • Are there observable gender differences by type of profile? • How do they relate with performativity (i.e., publications and citation impact)? GOALS MOTIVATION
  5. 5. 5 How are research careers assessed in academia? Scientific leadership VS. Team science • The number of middle authors is raising (Mongeon et al., 2017) • Middle authors tend to have shorter career trajectories (Milojević et al., 2018) The underlying assumptions of these studies is that author order in publications reflect leadership and that scientists specialise on specific tasks STARTING POINT MOTIVATION
  6. 6. 6 Author order reflects author contribution to scientific studies • There is a relation, but it is not always consistent (Sauermann & Haeussler, 2017) • Contributions do not reflect importance or level of involvement (Sauermann & Haeussler, 2017) Middle authors conduct technical tasks • U-shaped relation between author order and conceptual contributions (Larivière et al., 2016) • Increasing variety of technical contributions (Larivière et al., 2020) Seniority is related to types of contributions • Seniority is also reflected in author order and contributorship (Larivière et al., 2016) These studies look at author-publication combinations, but do not look directly into individual profiles UNDERLYING ASSUMPTIONS MOTIVATION
  7. 7. 7 Can we predict the probability of contribution of an author? • We include two types of variables: individual level and publication level • We apply the predictive model to the complete publication history of a set of researchers Can we profile researchers based on their predicted contributorships? • Career trajectories are defined by first year of publication and divided into four career stages • For each career stage we apply Robust Archetypal Analysis and assign researchers to archetypes How do profiles refer to research careers? • We look into career length, gender differences, productivity and citation impact STUDY DESIGN RESEARCH QUESTIONS
  8. 8. 8 DATA AND METHODS PROCESS FLOW 1 SEED DATASET Combination of bibliometric variables and contribution statements 2 3 4 5 PREDICTION MODEL Bayesian Networks to model data Cross-validation of predictions TRAJECTORIES DATASET Gender identification Break down by career stages ≥ 5 publications IDENTIFICATION OF PROFILES Robust Archetypal Analysis Assignment of researchers to archetypes PERFORMANCE BY PROFILES Career length Gender Productivity and impact Author order
  9. 9. 9 SEED DATASET DATA AND METHODS 70,694 publications PLOS journals Medical and Life Sciences Contribution statements from API (Larivière et al., 2016) Matching CWTS-in house Web of Science Only pubs with all authors identified Match by disambiguated author (Caron & van Eck, 2014) Composition of the dataset Contribution statements* Individual level – YE | PU Publication level – PO | AU | DT | CO | IN *Two were removed
  10. 10. 10 A. Junior < 5 y; Early- ≥ 5 > 15 y; Mid- ≥ 15 > 30 y; Late- ≥ 30 Declining technical contributorships over time WR and CE more stable but decline in late-career B. First authors most weight except CT for middle authors who contribute to technical contributorships Last authors on WR and CE COMMENTS DATA AND METHODS SEED DATASET
  11. 11. 11 DATA AND METHODS BAYESIAN NETWORKS BN graphically depicts interactions among dependent multivariate data. Directed acyclic graph (DAG), nodes represent random variables and arcs encode direct influences Max-Min Hill-Climbing (MMHC) algorithm Combination of score-based and constraint-based algorithms Use of a white-list Directionality of arcs Robustness checks Bootstrapping with replacement (50), threshold > 80% K-fold cross-validation (10 subsets)
  12. 12. 12 DATA AND METHODS ROBUST ARCHETYPAL ANALYSIS (RAA) Data aggregation of predicted contributorships • Median value of contributorships by career stage Archetypes as extreme observations in a multivariate dataset • RAA is less sensitive to outliers • Archetypes not exclusive • RAA is not a clustering techniques Assignment to archetypes • We use α-scores to assign researchers by career stage
  13. 13. 13 FINDINGS DIFFERENCES ON PREDICTED CONTRIBUTORSHIPS BY CAREER STAGE The model seems to discriminate by career stage Notable differences by type of contribution
  14. 14. 14 FINDINGS PARAMETERS OF ARCHETYPES BY CAREER STAGE • Similarity of profiles between career-stages • Leader profile defined by highest values on WR and CE • Specialized profile as the one performing the experiments • Supporting role may have a different meaning at late-career stage COMMENTS
  15. 15. FINDINGS PROFILES AND CAREER LENGTH 15
  16. 16. FINDINGS PROFILES AND PERFORMANCE 16 Large effect size Medium effect size
  17. 17. FINDINGS PROFILES AND GENDER 17 • 43% and 77% men have a leader profile in early- and mid-career stages • 27% and 65% women have a leader profile in early- and mid- career stages • Gender differences for leaders and specialized at these stages have a medium effect size COMMENTS
  18. 18. FINDINGS PROFILES AND AUTHOR ORDER 18 • Middle authorships largest share irrespective of profile but with differences by profile • Specialists similar shares of 1st author as leader, but not as last authors • Similar distributions at late-career stage COMMENTS
  19. 19. CONCLUSIONS IMPLICATIONS 19 • Task specialization seems to affect career prospects • Leading profiles seem to be more versatile than others • Bibliometric indicators seem to undermine specific profiles • Gender differences observed at early-career stages could be related to task specialization
  20. 20. CONCLUSIONS CAUTIONARY REMARKS 20 • Representativeness of the sample • Identification of scientists • Appropriateness of the contribution taxonomy • Measuring uncertainty • Longitudinal analysis of archetypes
  21. 21. CONCLUSIONS CAUTIONARY REMARKS 21 We do not look into causality, although… Author response to reviewers
  22. 22. elrobin@ugr.es THANK YOU! QUESTIONS? ALSO FEEL FREE TO CONTACT ME AT: http://nrobinsongarcia.com @nrobinsongarcia
  23. 23. 23 ADDITIONAL NOTES SEED DATASET JOURNAL DISTRIBUTION
  24. 24. 24 ADDITIONAL NOTES FIELD DELIMITATION Fields are assigned based on the Dutch NOWT Classification linked to Web of Science Subject categories 3 levels – 7 broad fields, 14 fields and 34 subjects Broad field assigned based on the share of referenced journals More here https://www.cwts.nl/pdf/nowt_classification_sc.pdf
  25. 25. 25 ADDITIONAL NOTES MIXED CORRELATION MATRIX

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