The perfect hacking of journal peer review or… The fastest way to become a Highly Cited Researcher

Since the beginning of the 21st century, the names of great fraudsters have spread beyond the academic arenas, each one bringing their biographies, their practices and the astonished tale of the discovery of their misdeeds. This starisation of fraudsters should neither hide the existence of famous cases in the past1, nor the multitude of ordinary misdemeanors and misconduct daily taking place in the academic world, which is hardly different from other professional circles in this respect. Nevetheless, they deserve a place in the Hall of Hame of academic fraudsters; so, before addressing the case of our new champion Kuo-Chen Chou, let’s review a few exemplary figures of this Hall of Fame, in alphabetical order.

Yoshitaka Fujii (2012): enduring Japanese anesthesiologist who owns is world record holder for the number of articles retracted (183). He spent his career inventing data and despite a statistical analysis published in 2000 showing how “too nice” his numbers were, he was not really worried until 10 years later.2

Woo-Suk Hwang (2006) : amazing Korean veterinarian and biologist, specialized in stem cells and producer of the first human clone, announced by a publication in Science. After being accused of forcing his technicians to donate their eggs for his research, investigations revealed the total absence of human cloning. A national glory in South Korea and an international star, his public downfall was so brutal that he made the cover of Time Magazine.

Jan-Hendrik Schön (2002): industrious German physicist, working at Bell Labs on the limits of matter and life, able to co-author in less than two years seven papers in Nature, eight in Science and six in Physic Review. All of course have since been retracted, and it seems that all his research, including his thesis, was based more on his desire to stick to the expectations of theory or those of his colleagues than on the empirical results he claimed to have achieved.3

Diederik Stapel (2011) : extraordinary Dutch psychologist, whose social experiments always proved the hypotheses made… since they were never carried out, but fabricated on paper and computer. Denounced by a whistleblower from his team and 58 retracted articles later, he was the object of a sensitive New York Times portrait . His own production became an object of psychology of deception as his colleagues found small differences in style between his genuine articles and the fake ones.

From transparent peer review
to citation manipulation

These eminent members of the Hall of Fame, all men – women are an extremely small minority among the elected members – have produced “false science” but have neither massively plagiarized nor attacked the peer review system. They rather provided, as good forgers, the expected raw material to journals. However, over the last 10 years, there has been concern about how reviewers or publishers can indirectly influence the science produced, in a more subtle way. “Coerced citations”, “fake peer review” and “cartel citations” are all designations of practices that do not directly fudge the content of articles, but act on the margins by hacking into the journal peer review process.

So, the old criticisms about the misdeeds of anonymity in journal peer review4 were reborn at great expense and many debates about “transparent peer review” took place. Where in the past editors and authors were at the helm of these discussions, now the publishers are in charge and, above all, Elsevier. The company provided two in-house researchers with access to his back office and they were able to compare the bibliography of the manuscripts with those of the published articles and check whether added references were coauthored by reviewers of the manuscript. Unsurprisingly, the authors of When Peer Reviewers Go Rogue concluded that there was a manipulated citation, even if its level is quite low (0.79%).

At the same time, a research team led by the famous John Ioannidis sought to build a “clean” base of citations for the most cited researchers. For them, this meant being able to separate self-citations from the rest, and it was on this occasion that they made a surprising discovery: the staggering intensity in the level of self-citation of some colleagues. Indeed, as your level of citations rise, you expect that they are all the more coming from distant colleagues. Not for everybody:

Vaidyanathan, a computer scientist at the Vel Tech R&D Institute of Technology, a privately run institute, is an extreme example: he has received 94% of his citations from himself or his co-authors up to 2017 (…) He is not alone. The data set, which lists around 100,000 researchers, shows that at least 250 scientists have amassed more than 50% of their citations from themselves or their co-authors, while the median self-citation rate is 12.7% ((Nature, Hundreds of extreme self-citing scientists revealed in new database, 19th August 2019)).”

But then, what are the Highly Cited Researchers, whose numbers are one of the components of the Shanghai Ranking, actually doing? Are they renowned for their influential results or are they more adept at being manufacturers on the citation chain? Bibliometricians would say that a “high” self-citation rate is not necessarly a sign of fraud, but that a detailed inquiry woud be neeeded This is where we return to our newest member of the Hall of Fame, whose work is worthy of close consideration.

A perfect hacker,
always greedy for citations

It all starts with a mundane story: a reviewer asks for additional references in a manuscript. But the request itself is not so trivial: it consists of 35 references, the vast majority of which are co-signed by him, and he indicates that his recommandation to editors, on whether or not to accept the manuscript, will depend heavily on this inclusion. It should also be specified that it is not for a single review that this request is made, but for each of the manuscripts passed through his hands. In describing their decision to ban this unnamed reviewer, editors did not indicate how long this practice has existed. Indeed, it is unusual, to say the least, to request the addition of so many references, and one might question their own responsibility in this matter if it lasted as they reference to the “most recent reviews” seems to imply. To which they reply:

One might ask how this reviewer got away with submitting multiple reviews containing coercive requests for citation before being banned. The shortest explanation is that excessive self-citation demands are generally not seen as an ethical problem until a pattern is established, and a decentralized peer-review system is not amenable to detecting patterns“((Wren, Jonathan D., Alfonso Valencia, and Janet Kelso. “Reviewer-coerced citation: case report, update on journal policy and suggestions for future prevention.” (2019): 3217-3218.)).

And in fact they inquire in other journals, and that suggested the same pattern of behavirour for this reviewer. A year later, in early 2020, the investigation leads to an editorial in another journal, the Journal for Theoretical Biology, (JTB), which reveals perhaps the most complete case of manipulated citation to date. Indeed, the hacker is no longer a simple reviewer there, but a “handling editor” for JTB, which enables him to act at several stages of the manuscript, with a single objective: to accumulate citations.

  1. He took the charge of many manuscripts from his research centre to ensure that they are well treated (conflict of interest).
  2. He chose reviewers requested by the authors, or designated colleagues from his own centre (conflict of interest) or even reviewed them himself under a false name (ghost peer review).
  3. In many cases, with the return of the reviews, he would ask for the title of the article to be changed so that it explicitly refers to his own algorithm, as well as a discussion of his own work in the introduction and conclusion (coerced citations).
  4. As a result, he requested the addition of a very large number of references (up to more than 50) to the bibliography of the manuscript (coerced citations).
  5. Just before the acceptance of the manuscript, he was added as co-author of the article (gift authorship).

We therefore observe two complementary types of behaviour. On the one hand, hidden from the outside, it consists in hacking the flow of the peer review journal, capturing the evaluation process to ensure that the articles most “favourable” to its citation count are actually published – and sometimes with his coauthorship. On the other hand, visible to the authors and perhaps the editor-in-chief and publisher, the aim is to hack the byline, content and references of the manuscript by making imperative requests for inclusion. Thus, these ordinary manuscripts became articles loaded with citations from the hacker.

It can be noted that at this stage, the name of the reviewer is not given by JTB, which caused some to make educated guess on Twitter. News articles in Nature among others, soon follow and revealed his identity: Kuo-Chen Chou, a retired chinese-american biophysicist. We then learn that he has been for years a member of the Highly Cited Researcher “club”5. So, as usual, this extraordinary case will be treated as “rare”, counter-measures have been taken such as an algorithm written by one of the Bioinformatics editor. But the ordinary gaming will still happen, would it be in so-called predatory journals or “prestigious” publishers, with smarter colleagues less greedy on citations and not obsessed with the HCR club. Will you be one of them?6

  1. for example John Darsee, see Broad, William; Wade, Nicholas (1983), Betrayers of the Truth: Fraud and Deceit in the Halls of Science, London: Century Publishing, ISBN0-7126-0243-7 []
  2. For a quick view of this case, see Pontille, David, and Didier Torny. “Behind the scenes of scientific articles: defining categories of fraud and regulating cases.” (2012). []
  3. He was the subject of a wonderful book, Plastic Fantastic, ISBN 978-0-230-22467-4 []
  4. See David Pontille and Didier Torny, “The blind shall see! the question of anonymity in journal peer review.” Ada: A Journal of Gender, New Media, and Technology, No.4. doi:10.7264/N3542KVW (2014). []
  5. the Web of Science Group didn’t list him in 2019 as he had, like others, a high rate of self-citations but, as stated, “Although this list is updated and refreshed each year, a Highly Cited Researcher is always a Highly Cited Researcher—whether their name was included in 2013 or 2019.” []
  6. I am aware that this post contains two self-references but they won’t be counted in any database []

The short history of the h-index or… being one click away to determine whether you are a succesful scientist

Sometimes, newness really happens in the academic world. Take bibliometrics indicators: for at least a century they have been usally proposed and discussed by specialized scientists from a scientometrics background in their field journals (currently JASIST, Scientometrics, …) and nobody else cared, at least for some time. But in 2005, something very peculiar happened: the h-index was coined by a total stranger to that field and had an instant success, which endures until now. How did that happen and why such a success?1

Physicist J.E. Hirsch proposed the h-index in a working paper posted on August 3rd, 2005 on arXiv, the famous open archive developed by physicists in Los Alamos. In his manuscript, he discussed the issue of comprehensively evaluating a researcher and wished, in a radical way, to subsume their whole career into a simple and practical measurement: an integer number. To do so, he considered that the production and its uses had to be taken into account through a citation measurement. So, number h is the greatest number for which h articles by an author have at least h citations. For example, for 5 articles cited at least 5 times, h is 5; likewise, for 50 articles cited at least 50 times each, h is 50.

Yet, the use of algorithms concerning authors was not new. Eugene Garfield, the founder of the Institut for ScIentific Information (ISI), claimed to regularly predict Nobel prizes using the Science Citation Index and had then developed the “ISI highly cited”, presenting results for a tiny fraction of “top”researchers ; similarly, a few disciplines like economics and management had a long tradition of ranking “top authors”. But, to our knowledge, no algorithm had to that date been specifically designed to evaluate authors . This novelty also comes from the lack of consideration Hirsch displayed for the existing litterature: there were only four references in his manuscript and only one in the field.

This departing from the scientometric tradition enabled Hirsch to make several shifts. Firstly, he did not take into account journals in which articles are published, probably because in high energy physicists, journals are used to archive knowledge more than to make discoveries public. Secondly, he excluded the pitfalls of the number and order of coauthors, which are of little relevance in physics but crucial in biomedical research for individual evaluation. Thirdly, his index combined two elements considered as heterogeneous in the scientometric tradition: production on the one hand, and use on the other. And finally fourthly, whereas scientometricians are always very cautious about individual analysis and save it for “outliers”, Hirsch proposed a measurement which applies to all researchers, and as a cherry on the cake, argued that it would be of some use for the allocation of research funds.

How did he make such a bold move? Based on numbers crunched in the case of high-energy physicists, HIrsch forged a model of the “successful scientist”. As the result of his algorithm very much depends on the duration of a researcher’s career, the h divided by the number of years of the career was considered as a good indicator by Hirsch.


“An h index of 20 after 20 years of scientific activity, characterizes a successful scientist. […] an h index of 40 after 20 years of scientific activity, characterizes outstanding scientists, likely to be found only at the top universities or major research laboratories. […] an h index of 60 after 20 years, or 90 after 30 years, characterizes truly unique individuals”.


At that point, Hirsch woud have probably been considered as a bibliometrics crackpot, a talented physicist that happens to crunch citations numbers in his pastime and posting his “personal views” on a website.

An instant success,
an impressive series of implementations

To the surprise (and probably horror) of the scientometrics community, the h-index was taken up at a staggering rate. As soon as his manuscript had been available on ArXiv, Hirsch received extensive feedback from his physicist colleagues and, in view of the shared enthusiasm, an open archive specialized in high energy physics, SPIRES (Stanford Physics Information REtrieval System), implemented the algorithm on its dataset only two weeks later. The same day, August 17th, 2005, Nature presented Hirsch’s proposition and highlighted his colleagues’ enthusiasm, while an editorial entitled “Rating Games” discussed the respective role of metrics and peer review. Shortly afterwards, in November 2005, two of Hirsch’s colleagues published the manuscript as an article2 in the Proceedings of the National Academy of Sciences, thus confirming physicists’ keen interest in this new measurement.

The popularization of the h-index took a new turn a yer later with a bibliometric tool developed by Ann-Will Harzing, Publish or Perish (PoP). In October 2006, this management professor at the University of Melbourne put online a small software, operationalizing the h-index calculation. That way, for any author whose name is entered by the user, irrespective of the discipline, PoP calculates their h-index in a single click, based on the nascent Google Scholar dataset. This tool could be downloaded for free and has thus allowed the magic algorithm to reach users far beyond audiences specialized in scientometrics. Researchers and institutions adopted this bibliometric tool so fast that the British Medical Journal published a spoof article describing the different pathologies it generates. Meanwhile, in May 2007 Elsevier had included the h-index in Scopus; Thomson-Reuters likewise had changed its “ISI Highly Cited” and integrated this index into the WoS in 2008. Thus, in just three years, individual measurement became a practical operation drawing on bibliometric tools easily accessible to each researcher.

This cycle of implementation was provisionally finalized by the opening of Google Scholar Citations (GSC) in the summer of 2011. With this new service, every academic could create and have control on her/his profile page on Google Scholar, and could decide to make it public or not. Whatever the choice, GSC would then automatically compute three metrics:
the widely used h-index, the i-10 index, which is the number of articles with at least ten citations, and the total number of citations to your articles.
At this point, the definition of the h-index wasn’t anymore needed and thousands of academics quickly made theirs available online. As Paul Wouters and Rodrigo Costas soon noted in their 2012 manuscript, this was a typical example of what they named “technologies of narcissism”, a mirror through which you and others would look in order to measure your influence, evaluate your importance, worry about your deficiencies.

From journals to articles to authors:
new policies for evaluation

Then, despite professional scientometricians harshly criticizing the h-index for its crudeness, pointing out its variations and limits or taming it to make a g-index or a v-index, the utopic/dystopic vision of Hirsch had come into reality. Conversely, it is because of its very crudeness that made it easier to implement in databases & more readable to lay researchers. But its availability is not sufficient to explain the duration and intensity of uses – the orignial paper will probably pass the 10.000 citations mark in 2020. There are two main and very different reasons for its popularity, which we must analytically distinguish. The first one is the implementation of the algorithm to new objects, such as research groups or even journals. Confronted with its unexpected success and eager to use the new data avaialable, scientometricians got into an h-index frenzy3. While the dominant popular bibliometrics index, both within the community and outside of it, had been for 30 years the Journal Impact Factor (JIF) then prouced and owned by Thomson ISI, the growing success of the h-index made it a challenger in the bibliometrical index “market”. Many papers compared the pros and cons of each algorithm, based on different datasets.

Nevertheless, this “good index” competition shouldn’t hide a second reason for which the h-index became so popular and discussed. Its use was sustained by a political agenda in assesment and evaluation, best represented by the San Francisco Declaration on Research Assessment (DORA) published in 2013. This complex text is often subsumed as an anti-bibliometrics statement, in which signing institutions promise they won”t use the JIF as a way to evaluate research, would it be for hiring, promoting, giving grants, etc. Beyond this simple vision, there are more nuanced recommandations that oppose journal-based metrics, but don’t refuse bibliometrics as a whole. JIF is seen as a bad way to perform quantified assesment, where article-level metrics, whatever they are (citations, downloads, views, social networks mentions…) are more realistic of the “impact” of a given research.

The problem with these new metrics is that nobody really knows what they are used for and what they really mesure4. Consequently, the most popular article-level metric remains the number of citations in a given database (Web of Science, Scopus, Crossref, Google Scholar). Rather than summing these numbers for a given journal, the aggregation is made on a given author. It is so simple to perform on the same databases, that what was absurd a few years ago has become natural. The “h revolution” therefore went beyond Hirsch’s own vision on two points. Firstly, his h-index was to be used for senior scientists, it is now also being applied to/by early and mid-carrer researchers as a more “ethical way” to judge their impact. Secondly, its extension goes hand in hand with a potential transformation of the model of scientific communication, to a post-journal world, in which any kind of text could be cited and counted. Rather than highlighting the fact that you actually passed the test of supposed prestigious journals, you just now give your h number and academic age, so everybody checks whether you really are the successful scientist you pretend to be . ((Please cite selected papers of the author of this post so he may finally become one)).

  1. This post is partially adapted from Pontille David, Torny Didier, « La manufacture de l’évaluation scientifique. Algorithmes, jeux de données et outils bibliométriques », Réseaux, 2013/1 (n° 177), p. 23-61. DOI : 10.3917/res.177.0023 []
  2. There are almost no differences between the ArXiv manuscript from mid-August (V3) and the published PNAS paper []
  3. See for an early litterature review, Bornmann, Lutz, and Hans‐Dieter Daniel. “The state of h index research.” EMBO reports 10.1 (2009): 2-6. []
  4. See Haustein, Stefanie, Timothy D. Bowman, and Rodrigo Costas. “Interpreting” altmetrics”: Viewing acts on social media through the lens of citation and social theories.” on ArXiv []

The Political Economy of Academic Publications

This blog is part of a vast research program on the political economy of scientific publication, which has been strongly transformed over the last twenty years by the electronic dissemination of journals. It considers publishers, editorial committees and journals as socio-political actors to be studied in three complementary aspects detailed below.

Firstly, they are analysed as economic actors defining publishing markets. The conditions under which these markets were created have been the subject of much criticism, and strong transnational mobilisations around open access have been deployed, which has influenced the construction of public policies that are contrasted internationally. New economic models have emerged, of which direct payment by the authors (APC), is only the most visible, but not the most frequent. The multiplication of coloured labels (Green, Gold, Platinum, Bronze, Diamond) to designate these models does not fully account for their subtle differences, nor for the sustainability of the associated business model, compared to the classic subscription model which has led to a “serial crisis” over the last 20 years, with the massive increase in the cost of access to publications for libraries


Secondly, journals and publishers are studied as places of production, including innovations in evaluation technologies (open peer review, technical soundness based review…). In particular, it is the growing debate on post-publication peer review policies, including withdrawing articles, that will be examined, as well as the emergence of platforms for public discussion of their validity such as PubPeer. The question of the centrality of journals for peer review or their marginalization (overlay journals, recommendations…) will also be addressed.


Thirdly, journals are treated as places of valorisation, seeking to attract authors and promote their position through the use of different measures (citation, referencing, uses…), which they highlight or criticise. In addition to the recurring debates on the Journal Impact Factor, a measure that is currently much decried, there will be discussions on alternative metrics, or even on responsible metrics, which are supposed to better represent academic production and its uses.


These three aspects aim in particular at sheding light on new forms of self-regulation by academic actors (systematisation of advertising for the withdrawal of articles, generalisation of post-publication peer review, stigmatisation of predatory publishers, uses of creative commons licenses…), the innovative and argumentative work of publishers and platforms, whether public, para-public or private, and the redefinition of public policies in the field of academic publication.