This document proposes using science maps and the Mean Overlay Distance (MOD) metric to measure knowledge dynamics and transfer in three areas: 1) Tracking the development of scientific fields over time by comparing citation networks years apart. 2) Evaluating research impact by measuring how publications influence distant scientific fields. 3) Analyzing researcher careers and mobility by tracking changes in individuals' research profiles over time and with career changes. The MOD metric quantifies the average cognitive distance between two maps, showing how research profiles or fields have become more integrated or diffuse over time. Case studies and applications in research evaluation, scientific development, and career analysis are discussed.
Sándor Soós - Science maps as ways to indicate knowledge transfer
1. Science maps
as ways to indicate knowledge transfer
Soó
Sándor Soós
Dept. Science Policy and Scientometrics
Library and Information Centre of the Hungarian Acad. Sci.
George Kampis
Lorand Eötvös University, Budapest, Hungary
2. Overview
Preliminaries: A global map of science
Preliminaries: Research profiles
Preliminaries: Structural measures
Problems of measuring Knowledge dynamics
A flexible proposal
Application1: Development of science
Application2: Research evaluation
Application3: Career and mobility studies
3. A global map of science
A global science map from (Rafols, Porter & Leydesdorff, 2009),
based on WoS databases
Unit of analysis: ISI Subject Category (SC)
The map: the proximity network of SCs
Method: „bibliometric coupling” of SCs
Disciplines: clusters (factors) in the proximity network
4. Global Map of Science, 2007
221 SCI-SSCI Subject Categories
Env Sci & Tech
Agri Sci
Ecol Sci
Infectious
Diseases
Geosciences
Clinical Med
Chemistry
Matls Sci
Engineering
Biomed Sci
Cognitive Sci.
Health & Social Issues
Psychology
Physics
Computer Sci.
Business & MGT
Social Studies
Econ. Polit. & Geography
Rafols, Porter and Leydesdorff (2009)
5. Modelling research profiles
The science overlay technique
Position of an actor within
the scientific landscape=
Structure of its
research profile
Method: Mapping a set of
publications onto the
global map (basemap)
SCs related to the
publication record are
highlighted, indicating
their respective weights
6. Structural measures
Measuring multi- and interdisciplinarity (IDR) upon this model: the
Stirling index
Novelty: Three structural features accounted for:
Number of SCs („variety”)
Distribution of pubs over SCs („balance”)
Proximity/distance of constituent SCs („disparity”)
8. A flexible proposal
1
Mean Overlay Distance (MOD) =
n*m
n,m
∑ pi p j dij
i =1, j =1
The (average) distance between two overlay maps
based on pairwise (weighted) cognitive distances between constituent SCs
10. App1: development of science
MOD: measuring knowledge diffusion/integration through
citation networks (evolution of a scholarly discourse)
A detailed, large-scale case study: the species problem
16. App2: Research evaluation
MOD as an evaluative/impact measure
Usual impact measures: based on quantity
Absolute (number of cits)
Normalized (field-normalized relative impact)
Weighted (eigenfactor)
MOD in this context: scope of citation impact
17. App2: Research evaluation
MOD as an impact measure:
How far (distance) a publication gets from its own research field, i.e. what
effect it bears on the scientific landscape
Carley, S., & Porter, A. L. (2012). A forward diversity index. Scientometrics, 90(2), 407-427.
18. App3: career and mobility studies
Seldom addressed dimension of scientific careers and
mobility: development of a research profile
Important variable of econometric models on mobility:
Effect of profile dynamics on productivity or vice versa
(generalist or specialist strategies)
Effect of various mobility dimensions on a research profile
and vice versa
SISOB (Science in Society Observatorium) program, FP7,
Mobility use case
19. App3: career and mobility studies
The Stirling index as an aggregated/static measure of research profile
development: thematic mobility for a large sample of engineers (SISOB
case study) provided by SISOB partner Fondazione Rosselli (U Turin)
Sample distribution of thematic mobility
Sample distribution by average number
of coauthors
20. App3: career and mobility studies
MOD in this context: thematic mobility, dynamic version
Career stage (n)
1
n*m
Career stage (n+1)
n,m
∑ pi p j dij
i =1, j =1
21. Acknowledgement
This paper was supported by
the European Commission under the FP7 Science in Society
Grant No. 266588 (SISOB project),
the European Union and the European Social Fund
through project FuturICT.hu (grant no.: TÁMOP4.2.2.C-11/1/KONV-2012-0013),
the János Bolyai Research Scholarship of the
Hungarian Academy of Sciences.
