Citation metrics versus peer review: Google Scholar, Scopus

Citation metrics versus peer review: Google Scholar, Scopus

Citation metrics versus peer review: Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison Anne-Wil Harzing, Middlesex University, London Satu Alakangas, University of Melbourne, Australia 3 An amateur in bibliometrics (1): Journal Quality 1993: Conversation with Head of Department: How do I know which journals are the best journals, I have no clue? Jan 2000: Bradford Management Centre, UK: Why on earth are we using this stupid VSNU journal ranking list that ranks my JIBS publication C and all other IB journals D (just like Brickworks, magazine for the building trade). I am sure there are better journal rankings lists around July 2000: The first incarnation of my JQL is published on 2015: The 56th edition of the JQL with18 rankings, >100 ISI cites + 50,000 page visits/year

2009: AMLE Outstanding article of the year award for When Knowledge Wins: Transcending the Sense and Nonsense of Academic Rankings [most highly cited article in management in 2009] 2015: AMLE Disseminating knowledge: from potential to reality New open-access journals collide with convention How predatory Open Access journals completely distorted Thomson Reuters Highly Cited Academics ranking (see also esi_highcite.htm) 4 An amateur in bibliometrics (1): Citation analysis May 2006: University of Melbourne: Promotion application to professor rejected: you havent published enough in A-journals Oct 2006: Publish or Perish v1.0 released Jan 2007: Reapplied for promotion showing my work had more citation impact than that any of the other professors, recent or longstanding 2010: The Publish or Perish Book, self-published through Amazon Createspace, reviewed in Nature, Scientometrics and

JASIST 2015: 80th or so release of Publish or Perish, >180 ISI cites, 1.7 million page visits to date 26 April 2015: Wharton Research Data Services distributes the Publish or Perish Book at the AACSB conference 5 An amateur in bibliometrics (3): publishing in the field Published a range of papers relating to Google Scholar and WoS Harzing, A.W.; Wal, R. van der (2008) Google Scholar as a new source for citation analysis?, Ethics in Science and Environmental Politics, 8(1): 62-71 Harzing, A.W.; Wal, R. van der (2009) A Google Scholar h-index for Journals: An alternative metric to measure journal impact in Economics & Business?, Journal of the American Society for Information Science and Technology, 60(1): 41-46. Harzing, A.W. (2013) A preliminary test of Google Scholar as a source for citation data: A longitudinal study of Nobel Prize winners, Scientometrics, 93(3): 10571075. Harzing, A.W. (2013) Document categories in the ISI Web of Knowledge: Misunderstanding the Social Sciences?, Scientometrics, 93(1): 23-34. Harzing, A.W.; Alakangas, S.; Adams, D. (2014) hIa: An individual annual h-index to accommodate disciplinary and career length differences, Scientometrics,

99(3): 811-821. Harzing, A.W. (2014) A longitudinal study of Google Scholar coverage between 2012 and 2013, Scientometrics, 98(1): 565-575. Harzing, A.W.; Mijnhardt, W. (2015) Proof over promise: Towards a more inclusive ranking of Dutch academics in Economics & Business, Scientometrics, 102(1):727-749. Harzing, A.W. (2015) Health warning: Might contain multiple personalities. The problem of homonyms in Thomson Reuters Essential Science Indicators, 105(3):2259-2270 Scientometrics. 6 The lesson for academic careers? If you want something changed: take initiative, you can change things, even as an individual Being generous can sometimes bring unexpected benefits I provide many resources for free on my website and spend many hours every week responding to requests for assistance from all over the world Many academics now know my name, even though they dont know my research Be prepared for the inevitable confusion and downright nasty reactions

It doesnt work support requests (no internet connection, wrong searches etc.) Enter my publications in your Harzing system now! CV attached; you have ruined my career by not including my publication in your database We are going on strike tomorrow because of the Harzing index, everyone hates you You are discriminating against me because I am not white, your website should be taken down instantly; I dont understand why you still have a job (I refused personal telephone support after giving extensive email support to an academic who kept maintaining I was wrong and he knew better how Google Scholar worked than I did) Accept that your research hobby can overpower your real research Publishing in another field can be great fun and liberating 7 Increasing audit culture: Metrics vs. peer review Increasing audit culture in academia, where universities, departments and individuals are constantly monitored and ranked National research assessment exercises, such as the ERA

(Australia) and the REF (UK), are becoming increasingly important Publications in these national exercises are normally assessed by peer review for Humanities and Social Sciences Citations metrics are used in the (Life) Sciences and Engineering as additional input for decision-making The argument for not using citation metrics in SSH is that coverage for these disciplines is deemed insufficient in WoS and Scopus 8 The danger of peer review? (1) Peer review might lead to harsher verdicts than bibliometric evidence, especially for disciplines that do not have unified paradigms, such as the Social Sciences and Humanities In Australia (ERA 2010) the average rating for the Social Sciences was only about 60% of that of the (Life) Sciences This is despite the fact that on a citations per paper basis Australias worldwide rank is similar in all disciplines

The low ERA-ranking led to widespread popular commentary that government funding for the Social Sciences should be reduced or removed altogether Similarly negative assessment of the credibility of SSH can be found in the UK (and no doubt in many other countries) 9 The danger of peer review? (2) More generally, peer review might lead to what I have called promise over proof Harzing, A.W.; Mijnhardt, W. (2015) Proof over promise: Towards a more inclusive ranking of Dutch academics in Economics & Business, Scientometrics, vol. 102, no. 1, pp. 727-749. Assessment of the quality of a publication might be (subconsciously) influenced by the promise of: the journal in which it is published, the reputation of the author's affiliation, the sub-discipline (theoretical/modeling vs. applied, hard vs. soft) [Promise] Publication in a triple-A journal initially means that 3-4

academics thought your paper was a worthwhile contribution to the field. But what if this paper is subsequently hardly ever cited? [Proof] Publication in a C-journal with 1,000+ citations means that 1,000 academics thought your paper was a worthwhile contribution to the field 1 0 What can we do? Be critical about the increasing audit culture Adler, N.; Harzing, A.W. (2009) When Knowledge Wins: Transcending the sense and nonsense of academic rankings, The Academy of Management Learning & Education, vol. 8, no. 1, pp. 72-95. But: be realistic, we are unlikely to see a reversal of this trend. Hence in order to emancipate the Social Sciences and Humanities, an inclusion of citation metrics might help. However, we need to: Raise awareness about: Alternative data sources for citation analysis that are more inclusive (e.g. including books, local and regional journals, reports, working papers) Difficulty of comparing metrics across disciplines because of different publication and

citation practices Life Science and Science academics in particular write more (and shorter) papers with more authors each; 10-15 authors not unusual, some >1000 authors Suggest alternative data sources and metrics Google Scholar or Scopus instead of WoS/ISI hIa (Individual annualised h-index), i.e. h-index corrected for career length and number of co-authors measures the average number of single-author equivalent impactful publications an academic publishes a year (usually well below 1.0) Need for comprehensive empirical work Dozens of studies comparing two or even three databases. However: Focused on a single or small groups of journals or a small group of academics Only covered a small number of disciplines Largest study was Delgado-Lpez-Czar &Repiso-Caballero (2013), but only included a single discipline Very few studies doing longitudinal comparisons De Winter et al. (2014): WoS and GS 2005 & 2013 for 56 classic articles Harzing (2014): 2012-2013 for 20 Nobel Prize winners (GS only)

Hence our study provides: 2-year longitudinal comparison (2013-2015) with quarterly data-points Cross-disciplinary comparison across all major disciplinary areas Comparison of 4 different metrics: publications, citations, h-index hI,annual (h-index corrected for career length and number of co-authors) 1 1 The bibliometric study (1): The basics Sample of 146 Associate and Full Professors at the University of Melbourne All main disciplines (Humanities, Social Sciences, Engineering, Sciences, Life Sciences) were represented, 37 sub-disciplines Two full professors (1 male, 1 female) and two associate professors (1 male, 1 female) in each sub-discipline (e.g. management, marketing, accounting, economics) Collected data on education, career trajectory, international experience, internal/ external promotion, and career interruptions through survey (not reported here)

Citation metrics in WoS/ISI, Scopus and Google Scholar Collected citation data every 3 months for 2 years Google Scholar data collected with Publish or Perish ( WoS/ISI and Scopus collected in the respective databases and imported into Publish or Perish to calculate metrics The final conclusion: with appropriate metrics and data sources, citation metrics can be applied in the Social Sciences ISI H-index: Life Sciences average lies 200% above Social Sciences average GS hIa index: Life Sciences average lies 8% below Social Sciences average 1 2 The bibliometric study (2): Details on the sample Sample: 37 disciplines were subsequently grouped into five major disciplinary fields: Humanities: Architecture, Building & Planning; Culture & Communication, History; Languages & Linguistics, Law (19 observations), Social Sciences: Accounting & Finance; Economics; Education; Management &

Marketing; Psychology; Social & Political Sciences (24 observations), Engineering: Chemical & Biomolecular Engineering; Computing & Information Systems; Electrical & Electronic Engineering, Infrastructure Engineering, Mechanical Engineering (20 observations), Sciences: Botany; Chemistry, Earth Sciences; Genetics; Land & Environment; Mathematics; Optometry; Physics; Veterinary Sciences; Zoology (44 observations), Life Sciences: Anatomy & Neurosciece; Audiology; Biochemistry & Molecular Biology; Dentistry; Obstetrics & Gynaecology; Ophthalmology; Microbiology; Pathology; Physiology; Population Health (39 observations). Discipline structure followed Department/School structure at the University of Melbourne Overrepresentation of the (Life) Sciences and underrepresentation of Social Sciences beyond Business & Economics Overall, sufficiently varied coverage across the five major disciplinary fields 1 3 The bibliometric study (3): Descriptive statistics

1 4 1 5 Longitudinal results: quarterly % increase in papers per academic in different databases 1 6 Longitudinal results: quarterly % increase in citations per academic in different databases 1 7

1 8 Different data-sources between disciplines: number of papers 200 180 Number of papers 160 140 120 100 80 60 40 20 0 Humanities

Social Sciences Engineering Sciences Life Sciences 1 9 Different data-sources between disciplines: number of citations 5000 4500 4000 3500 Citations

3000 2500 2000 1500 1000 500 0 Humanities Social Sciences Engineering Sciences Life Sciences Different data-sources between disciplines: number of citations 5000

4000 Citations 3000 2000 1000 0 Web of Science Scopus Google Scholar 2 0

Different data-sources between disciplines: h-index h-index 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Web of Science

Scopus Google Scholar 2 1 hIa index Different data-sources between disciplines: hIa index 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

Web of Science Scopus Google Scholar hIa: h-index corrected for academic age (to accommodate differences in career length) and number of co-authors (to remove discipline bias) 2 2 Comparing WoS h-index with Scopus or GS hIa 2 3 Different data-sources between disciplines: Statistics For the ISI h-index gender, rank and discipline differences explain nearly 60% of the variance

For GS hIa, the explained variance is only 14% Reduction of differences across levels of appointment Reduction of differences across disciplines ISI h-index Google Scholar hIa Stand. Beta Significance Stand. Beta Significance Gender = Female -0.066 0.222 -0.017

0.822 Rank Professor 0.361 0.000 0.217 0.006 Humanities -0.591 0.000 -0.356 0.000

Social Sciences -0.491 0.000 0.020 0.816 Engineering -0.357 0.000 -0.149 0.087 Sciences

-0.045 0.468 -0.123 0.178 Adjusted R-square 0.591 0.139 2 4 Quick comparison across disciplines H-index ISI data Life Sciences vs. Humanities: 27 vs. 3.5

i.e. nearly 8 times as high Life Sciences vs. Social Sciences: 27 vs. 9.5 i.e. nearly 3 times as high hIa-index GS data Life Sciences vs. Humanities: 0.61 vs. 0.34 i.e. nearly 2 times as high Life Sciences vs. Social Sciences: 0.61 vs. 0.66 i.e. 8% lower 2 5 Individual comparisons for the three databases 2 6 2 7

Conclusion Will the use of citation metrics disadvantage the Social Sciences and Humanities? Not, if you use a database that includes publications important in those disciplines (e.g. books, national journals) Not, if you correct for differences in co-authorships Is peer review better than metrics (in large scale research evaluation)? Yes, in a way. The ideal version of peer review (informed, dedicated, and unbiased experts) is better than a reductionist version of metrics (ISI h-index or citations) However, the inclusive version of metrics (GS hIa or even Scopus hIa) is probably better than the likely reality of peer review (hurried semiexperts, potentially influenced by journal outlet and affiliation) In research evaluation at any level use a combination of peer review and metrics wherever possible, but: If reviewers are not experts, metrics might be a better alternative If metrics are used, use an inclusive database (GS or Scopus) and career and discipline adjusted metrics 2

8 Want to know more? The resulting article has been resubmitted to Scientometrics yesterday after a second round of revisions So hopefully it will be accepted and in press soon Any questions or comments?

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