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

lanning@cal.berkeley.edu

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

The organizational model: Advantages and limitations Existing U

Arts and Letters

Anthro

Baker Cavell Douglas

English

Science

Biology

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

Towards an empirical approach

A pilot studyLimitations: thin data, self-report

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

# 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.

# 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.

# 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

# 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

The network of departments:Global and clique-based

perspectives

The communities of departments bear little resemblance to the existing colleges of the university

# Communities (k-cliques) of departments:

Communities (k-cliques) of persons I:The interdisciplinary core

Who are the knowledge conduits?

Nodes ranked by Betweenness Centrality,data are University-wide

Who belongs to multiple communities?

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

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.