1. Sisob conceptual model Richard Walker May 30, 2011

2. Overview • Goals • General approach • Entities • Operationalizing model • D2.2 Table of Contents

3. Goals • Common vocabulary and approach • Homogeneous approach to case studies

4. General approach • Inspired by computer science • Abstract classes for networks, actors and outcomes • Each class has attributes • Real actors in case studies are instances of abstract classes • Measurements give value to attributes

5. Entities • Production • Actors • Networks • Context • Distribution • Actors • Networks • Context • Consumption • Actors • Networks • Context • Outcomes • Scientific • Economic • Social

6. Operationalizing model • What are the questions I want to ask? • What are the operational entities relevant to my model? • Example • Who are the production actors? • What are the production networks? • What are my data sources? • Do I already have access to the data I need? • Do I need crawling / data from other partners • How do I characterize my entities using my data sources? • Example • What measurements do I use to characterize networks

7. D2.2 Table of Contents • 1Objectives and structure of this document • 2The Impact of science on society (Frontiers with contributions from all partners) • 3The SISOB conceptual mode • 3.1Goals of the model (Frontiers with input from all partner) • 3.2Overview • 3.3Entities in the model (Frontiers UDE and ELTE for measurements and UM for tools, all partners) • 4Operationalizing the model – Researcher Mobility (Unito) • 4.1Background • 4.2Goals and hypotheses of the case study • 4.3The model – an overview • 4.4Model entities • 5Operationalizing the model – Knowledge Sharing (UDE) • 5.1Background • 5.2Goals and hypotheses of the case study • 5.3The model – an overview • 5.4Model entities • 6Operationalizing the model – Literature review (Frontiers) • 6.1Background • 6.2Goals and hypotheses of the case study • 6.3The model – an overview • 6.4Model entities • 6.4.1 Appendix 1: Requirements on SISOB tools • Summary of required measurements • Summary of required measurement tools • REFERENCES • Appendix A: Common Network Indicators

8. Peer review – data requirements Richard Walker

9. Overview • Scientific questions • Operationalization of conceptual model • Sample hypotheses • Data sources required

10. Scientific questions • How does peer review affect impact of science • “Traditional” issues • Cognitive biases • Traditional cronyism • “Cognitive cronyism” • “Social” issues • How do relationships among reviewers affect review process? • How do relationships among reviewers and authors affect the review process? • “New” issue • How do new models of review affect review process?

11. Operationalizing the conceptual model

12. Sample hypotheses • Traditional • Papers with a woman as a first author are more likely to be accepted review committee includes a woman • Social • Papers are more likely to be accepted if authors are “close” to reviewers in author-reviewer network (cognitive cronyism) • Different techniques of reviewing • Open reviewing (Frontiers) is less affected by bias x than traditional reviewing

13. Data sets required/1 • Frontiers papers • Attributes of papers, authors, reviewers • Source: Frontiers • Use: reconstruct author and reviewer networks • All papers by Frontiers authors and reviewers (last 10 years) • Source: crawling • Use: enhance author and reviewer networks • All citations of papers in Frontiers data set • Source: crawling • Use: outcome measurement • Productivity of authors and reviewers • Measirement: number, citations, outside references • Source: crawling • Use: outcome measurement • Non-academic citations of papers in Frontiers data set • Source: crawling • Use: outcome measurement

14. Data sets required • Conference papers (UMA) • Attributes of papers, authors, reviewers • Source: ?? • Use: reconstruct author and reviewer networks • All papers by authors and reviewers (last 10 years) • Source: crawling • Use: enhance author and reviewer networks • All citations of papers in data set • Source: crawling • Use: outcome measurement • Productivity of authors and reviewers • Measirement: number, citations, outside references • Source: crawling • Use: outcome measurement • Non-academic citations of papers in data set • Source: crawling • Use: outcome measurement

15. Open issues • Availability of data sources • Network indicators • BIG ISSUE 1 – how do we make this useful for policy makers? • BIG ISSUE 2 – how do look this in a community perspective