University Rankings,

the Triple Helix Model

and Webometrics:

Opening Pandora’s Box

Prof. Han Woo Park

Dept of Media & Communication, Yeungnam Univ

Pieter Stek

Doctoral Student, Delft University of Technology

Disclaimer

The views expressed in this presentation

reflect personal opinions which may or may

not coincide with the views of the

organization with which the authors are

affiliated.

Aims

• Understanding the methodologies and

impact of existing university rankings

• Discussing how advances in Webometrics

may lead to new university rankings

• Opening Pandora’s box: towards a Triple

Helix Ranking of Universities

Agenda

• Introducing university rankings

• Webometrics of academia

• A Triple Helix Ranking concept

– Debating the pros and cons

University rankings

Which is your favorite?

Rankings compared

Criterion QS THE Shanghai Leiden

Academic

publications, Nobel,

Fields

20% 36% 90% 100%

Reputation survey 50% 33%

Faculty/student ratio 20% 4.5%

International 10% 7.5%

Other 8.25% (doctor-bachelor

ratio)

2.5% (industry income)

8.25% (income)

10% (per

capita

performan

ce)

Some national rankings

• Joongang Ilbo (Korea)Faculty research (33%), education and financial (30%), reputation and alumni (20%), internationalization (17%)

• U.S. News and World Report (USA)Reputation (22.5%), selectivity (12.5%), faculty resources (20%), graduation/retention rates (30%), financial (15%)

• Zeit CHE Ranking (Germany)At course level, course features + student and faculty opinion survey

• Research Assessment Framework (UK)At department level, focusing on ‘originality, significance and rigor’, thus including research impact

• Etc.

Alternative: Trojan Condom Ranking

Pa

rty

Sch

oo

l R

an

kin

g

Rankings summary & claims

• Matter to students, parents, employers and

governments

• So they matter to universities

• Propositions:

– Rankings are changing universities

– Rankings are a policy tool

– Rankings reflect consumer power

Webometrics of academia

Internet presence

• Essential questions:– Who is mentioned? (content analysis)

– By whom? (citation network analysis)on the internet

– What does this say about the underlying academic system?

• We present some findings published in Scientometrics:– Barnett et al. (2013) – [B]

– Lee & Park (2012) – [L]

– Chung & Park (2012) – [C]

Universities

• [B] Universities network centrality on the academic internet (e.g. harvard.edu, tudelft.nl, yu.ac.kr) has a statistically significant correlation with:– University size

– Number of Nobel Prizes

– Rankings (U.S. News)

– Doctoral degrees yes/no

– English-speaking yes/no

– Bandwidth capacity

– Physical distance is irrelevant

• And at the national level:– Citations, co-authorship, student exchange, total number of

weblinks

Webometrics rankings?

• They exist: webometrics.info, which is

based on an inbound link measure

• [B] Academic web network centrality is

predictive of rankings (U.S. News)

• [L] Web visibility is also highly predictive of

rankings (Shanghai)

• There is an ‘English speaking’ bias/benefit

Scholars

• [C] Online visibility of scholars also

correlates to their SSCI output

• There is again an ‘English speaking’

bias/benefit

Webometrics summary & claims

• Web indicators (content and network) correlate to other academic performance measures

• Webometrics are regarded as ‘reliable’, but not all links and content are valid

• Propositions:

– Who links to you is what you are

– Web presence matters for universities and individual scholars

– Web presence should be part of university’s institutional strategy

A Triple Helix Ranking concept

Triple Helix interaction matters for…

• Students & parents: get a job

– For some: become entrepreneurs

• Academics: more money for research

• Companies: better innovation

• Government: happy people and companies

– and innovation eventually grows the tax base

Some indicators

• Co-authorship across TH sectors

• Citations of scientific documents in patents

• Mentions of university in industry/government media/websites (and vice-versa?)

• Production of patents by/with universities

• Number of start-ups from/near the university

• Industry R&D funding

• Employability

• Mobility of researchers across TH sectors

Likely pitfalls

• Taking into account local context

– Matters little to students and parents

– Matters to government, local people

• Unintended side-effects of ranking strategies

– Is the Triple Helix a means or an end?

• Too similar to current rankings: nothing new

• Differences between fields

– e.g. theatre vs. electrical engineering

– one size does not fit all

The Triple Helix: back to basics

• Three sectors (strands) – industry, university and government…– or more? – international, user/consumer

• Co-evolution – through communication between members of different sectors

• Balance – no single actor is dominant, they lead together

• Benefits – for all actors involved, and the innovation ecosystem

• Multiscalar – acts on different scales

The Customized Helix-Beyond UIG

• What are the main Helix ‘strands’ in

different sectors?

• Some suggestions:

Theatre NursingEngineering

(“traditional” TH)

Actors and writers

Audience

Producers

Government

(censorship, subsidy)

Hospitals & doctors

Patients

Nursing School

Regulator

Companies

Consumers/Users

Universities

Government

The Customized Helix

• Even differentiation within fields:

Accounting Marketing Human Resources

Accounting board

Investors

Tax agency

Business school

Firms

Advertising agencies

Consumers

Business school

Labour unions

Firm management

Labour regulation

Business school

The role of webometrics

• Provide large-scale quantitative evidence

to understand/confirm cross-sector

interactions taking place

• Versatility in data sources, i.e. goes beyond

patents and academic articles

• Considers both network and content

Propositions

1. The strength of the Triple Helix lies in it being a conceptual model

– What the sectors are, is of secondary importance

2. A Triple Helix ranking can be a hybrid tool for policy makers, academics and students alike

3. Webometrics is less biased than other indicators of university quality

Time’s up!

• Thank you for participating.

Should you wish to get in touch with us:

• Prof. Han Woo Park – hanpark@ynu.ac.kr,

www.hanpark.net

• Pieter Stek – p.e.stek@tudelft.nl