university rankings,the triple helix modeland webometrics:opening pandora’s box

University Rankings,the Triple Helix Modeland Webometrics:Opening Pandora’s Box

Prof. Han Woo ParkDept of Media & Communication, Yeungnam Univ

Pieter StekDoctoral 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.

https://www.triplehelixassociation.org/webinar-series/university-rankings

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 LeidenAcademic 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 performance)

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

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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 Nursing Engineering(“traditional” TH)

Actors and writersAudienceProducers

Government (censorship, subsidy)

Hospitals & doctorsPatients

Nursing SchoolRegulator

CompaniesConsumers/Users

UniversitiesGovernment

The Customized Helix

• Even differentiation within fields:

Accounting Marketing Human ResourcesAccounting board

InvestorsTax agency

Business school

FirmsAdvertising agencies

ConsumersBusiness school

Labour unionsFirm managementLabour regulationBusiness 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