scits 2014
TRANSCRIPT
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Can bibliographic couplings inform the structure of large public universities?
Kevin Lanning Xingquan ZhuFla Atl U
Note: Slides preceded by # were not included in the presentation due to time
constraints.
Updated analyses and a more detailed report are available from
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Background: A network of disciplines
Higher Ed Administration
Social sciences Psychology Personality
Data sciences SNA Bibliometrics
How can an empirical approach inform the structure of a university?
Can preferential attachment - a feature of networks and a pervasive source of Inequality - be overcome?
How can the social system of the university be change to better serve people?
People are ‘real,’ though transient, disciplines are constructions, though enduring
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The organizational model: Advantages and limitations Existing U
Arts and Letters
Anthro
Baker Cavell Douglas
English
Science
Biology
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An a priori model
Three broad, interconnected, hierarchically articulated themes• Trust• Peace studies, diplomacy, cybersecurity
• Preparedness• Disaster, climate change, trauma
• Vulnerable populations• Healthy aging, immigration, early childhood
The model…fails
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Towards an empirical approach
A pilot studyLimitations: thin data, self-report
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The EBRP project
Bibliographic couplings 8000 papers, 108000 papers cited thereinA bipartite graph Univ scholar-> cited papers <-Univ scholarIn most analyses, projected onto one mode Univ scholar <-> Univ scholar
Without the Elsevier Bibliometric Research Program (EBRP), this work would not have been done
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# Some concerns and clarifications
Not a study of impact or reputation, but engagement (citing rather than being cited)
Not a map of science, but of a communityPersons are focal units
The goal is to build social and intellectual capital
We expect that the approach will have little utility in the arts and humanities.
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# Some indeterminacies
No single approach to weighing order of authorship
A loss of information as one moves from the bipartite to a single mode network
Persons vs papers as targets in the initial network
Etc.
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# Tools
Database software (Access)Extensive cleaning, removal of duplicates, non-faculty authored papers, and disambiguating of shared names
J. Smith -> Smith J1, Smith J2, …Gephi
For network properties and visualizationsMMNT plugin
C-finder (Palla, cfinder.org)For finding and displaying overlapping communities
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# Four approaches to representing the network of scholars
Left panels: Bipartite networks (referenced papers are hidden)Right panels: Single mode projection
Top panels: Targets are individual referencesBottom panels: Targets are individual authors
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The network of departments:Global and clique-based
perspectives
The communities of departments bear little resemblance to the existing colleges of the university
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# Communities (k-cliques) of departments:
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Communities (k-cliques) of persons I:The interdisciplinary core
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Who are the knowledge conduits?
Nodes ranked by Betweenness Centrality,data are University-wide
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Who belongs to multiple communities?
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Are the conduits (betweenness) and thebrokers (community bridgers) the same people?
Tenured Gender Clique.. Bet… W. Deg. EC PRTenured 1.000Gender -0.186 1.000Clique bridge -0.005 -0.037 1.000Betweenness C 0.059 0.031 0.482 1.000Weighted Degree 0.047 0.030 0.493 0.580 1.000Eigenvector C 0.046 0.006 0.190 0.524 0.348 1.000PageRank 0.052 0.030 0.448 0.658 0.872 0.446 1.000
N=346. n "clique bridges" = 13
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Closing thoughtsWith respect to the title of the talk:
There is strong evidence that potentially productive communities exist outside of academic units
With respect to this talk in SciTS:The concept of (knowledge broker, bridge, gatekeeper) is multiply nested, and can be represented at multiple levels of analysis.
With respect to this talk in the world:In a time of building walls between people, social network analysis can open doors.