34C3 preroll music Herald: Please give a warm welcome here. It’s Franziska, Teresa, and Judith. Judith, you have the stage, thank you. Judith Hartstein: Thank you, thanks! applause inaudible Judith: We believe that scientific performance indicators are widely applied to inform funding decisions and to determine the availability of career opportunities. So, those of you who are working in science or have had a look into the science system might agree to that. And we want to understand evaluative bibliometrics as algorithmic science evaluation instruments to highlight some things that do occur also with other algorithmic instruments of evaluation. And so we’re going to start with a quote from a publication in 2015 which reads “As the tyranny of bibliometrics tightens its grip, it is having a disastrous effect on the model of science presented to young researchers.” We have heard the talk of hanno already, and he’s basically also talking about problems in the science system and the reputation by the indicators. And the question is, is bibliometrics the bad guy here? If you speak of ‘tyranny of bibliometrics’, who is the actor doing this? Or are maybe bibliometricians the problem? We want to contextualize our talk into the growing movement of Reflexive Metrics. So those who are doing science studies, social studies of science, scientometrics and bibliometrics. The movement of Reflexive Metrics. So the basic idea is to say: “Okay, we have to accept accountability if we do bibliometrics and scientometrics.” We have to understand the effects of algorithmic evaluation on science, and we will try not to be the bad guy. And the main mediator of the science evaluation which is perceived by the researchers is the algorithm. I will hand over the microphone to… or I will not hand over the microphone but I will hand over the talk to Teresa. She’s going to talk about "Datafication of Scientific Evaluation". Teresa Isigkeit: Okay. I hope you can hear me. No? Yes? Okay. Judith: mumbling When we think about the science system what do we expect? What can society expect from a scientific system? In general, we would say reliable and truthful knowledge, that is scrutinized by the scientific community. So where can we find this knowledge? Normally in publications. So with these publications, can we actually say whether science is bad or good? Or is there better science than others? In the era of digital publication databases, there’s big datasets of publications. And these are used to evaluate and calculate the quality of scientific output. So in general, with this metadata we can tell you who is the author of a publication, where is the home institution of this author, or which types of citations are in the bibliographic information. This is used in the calculation of bibliometric indicators. For example if you take the journal impact factors, which is a citation based indicator, you can compare different journals. And maybe say which journals are performing better than others or if the journal factor has increased or decreased over the years. Another example would be the Hirsch-Index for individual scientists, which is also widely used when scientists apply for jobs. So they put these numbers in their CVs and supposedly this tells you something about the quality of research those scientists are conducting. With the availability of the data we can see an increase in its usage. And in a scientific environment in which data-driven science is established, scientific conduct decisions regarding hiring or funding heavily rely on these indicators. There’s maybe a naive belief that these indicators that are data-driven and rely on data that is collected in the database is a more objective metric that we can use. So here's a quote by Rieder and Simon: “In this brave new world trust no longer resides in the integrity of individual truth-tellers or the veracity of prestigious institutions, but is placed in highly formalized procedures enacted through disciplined self-restraint. Numbers cease to be supplements.” So we see a change of an evaluation system that is relying on expert knowledge to a system of algorithmic science evaluation. In this change there’s a belief in a depersonalization of the system and the perception of algorithms as the rule of law. So when looking at the interaction between the algorithm and scientists we can tell that this relationship is not as easy as it seems. Algorithms are not in fact objective. They carry social meaning and human agency. They are used to construct a reality and algorithms don’t come naturally. They don’t grow on trees and can be picked by scientists and people who evaluate the scientific system, so we have to be reflective and think about which social meanings the algorithm holds. So when there is a code that the algorithm uses, there is a subjective meaning in this code, and there is agency in this code, and you can’t just say, oh, this is a perfect construction of the reality of scientific system. So the belief that this tells you more about the quality of research is not a good indicator. So when you think about the example of citation counts the algorithm reads the bibliographic information of a publication from the database. So scientists, they cite papers that relate to their studies. But we don’t actually know which of these citations are more meaningful than others, so they’re not as easily comparable. But the algorithms give you the belief they are, so relevance is not as easily put into an algorithm and there is different types of citations. So the scientists perceive this use of the algorithms also as a powerful instrument. And so the algorithm has some sway above the scientists because they rely so much on those indicators to further their careers, to get a promotion, or get funding for their next research projects. So we have a reciprocal relationship between the algorithm and the scientists, and this creates a new construction of reality. So we can conclude that governance by algorithms leads to behavioral adaptation in scientists, and one of these examples that uses the Science Citation Index will be given from Franziska. Franziska Sörgel: Thanks for the handover! Yes, let me start. I’m focusing on reputation and authorship as you can see on the slide, and first let me start with a quote by Eugene Garfield, which says: “Is it reasonable to assume that if I cite a paper that I would probably be interested in those papers which subsequently cite it as well as my own paper. Indeed, I have observed on several occasions that people preferred to cite the articles I had cited rather than cite me! It would seem to me that this is the basis for the building up of the ‘logical network’ for the Citation Index service.” So, actually, this Science Citation Index which is described here was mainly developed in order to solve the problems of information retrieval. Eugene Garfield, also founder of this Science Citation Index – short: SCI – noted or began to note a huge interest in reciprocal publication behavior. He recognized the increasing interest as a strategic instrument to exploit intellectual property. And indeed, the interest in the SCI – and its data – successively became more relevant within the disciplines, and its usage extended. Later, [Derek J.] de Solla Price, another social scientist, asked or claimed for a better research on the topic, as it currently also meant a crisis in science, and stated: “If a paper was cited once, it would get cited again and again, so the main problem was that the rich would get richer”, which is also known as the “Matthew Effect”. Finally, the SCI and its use turned into a system which was and still is used as a reciprocal citation system, and became a central and global actor. Once a paper was cited, the probability it was cited again was higher, and it would even extend its own influence on a certain topic within the scientific field. So it was known that you would either read a certain article and people would do research on a certain topic or subject. So this phenomenon would rise to an instrument of disciplining science and created power structures. Let me show you one example which is closely connected to this phenomenon I just told you about – and I don’t know if here in this room there are any astronomers or physicists? Yeah, there are few, okay. That’s great, actually. So in the next slide, here, we have a table with a time window from 2010 to 2016, and social scientists from Berlin found out that the co-authorship within the field of physics extended by 58 on a yearly basis in this time window. So this is actually already very high, but they also found another very extreme case. They found one paper which had roundabout 7,000 words and the mentioned authorship of 5,000. So, in average, the contribution of each scientist or researcher of this paper who was mentioned was 1.1 word. Sounds strange, yeah. And so of course you have to see this in a certain context, and maybe we can talk about this later on, because it has to do with Atlas particle detector, which requires high maintenance and stuff. But still, the number of authorship, and you can see this regardless which scientific field we are talking about, generally increased the last years. It remains a problem especially for the reputation, obviously. It remains a problem that there is such high pressure on nowadays researchers. Still, of course, we have ethics and research requires standards of responsibility. And for example there’s one, there’s other ones, but there’s one here on the slide: the “Australian Code for the Responsible Conduct of Research” which says: “The right to authorship is not tied to position or profession and does not depend on whether the contribution was paid for or voluntary. It is not enough to have provided materials or routine technical support, or to have made the measurements on which the publication is based. Substantial intellectual involvement is required.” So yeah, this is, could be one rule to work with or to work by, to follow. And still we have this problem of reputation which remains, and where I hand over to Judith again. Judith: Thank you. So we’re going to speak about strategic citation now. So if you put this point of reputation like that, you may say: So the researcher does find something in his research, his or her research, and addresses the publication describing it to the community. And the community, the scientific community rewards the researcher with reputation. And now the algorithm, which is like perceived to be a new thing, is mediating the visibility of the researcher’s results to the community, and is also mediating the rewards – the career opportunities or the funding decisions etc. And what happens now and what is plausible to happen is that the researcher addresses his or her research also to the algorithm in terms of citing those who are evaluated by the algorithm, who he wants to support, and also in terms of strategic keywording etc. And that’s the only thing which happens new, might be a perspective on that. So the one thing new: the algorithm is addressed as a recipient of scientific publications. And it is like far-fetched to discriminate between so-called and ‘visible colleges’ and ‘citation cartels’. What do I mean by that? So ‘invisible colleges’ is a term to say: “Okay, people are citing each other. They do not work together in a co-working space, maybe, but they do research on the same topic.” And that’s only plausible that they cite each other. And if we look at citation networks and find people citing each other, that does not have necessarily to be something bad. And we also have people who are concerned that there might be like ‘citation cartels’. So researchers citing each other not for purposes like the research topics are closely connected, but to support each other in their career prospects. And people do try to discriminate those invisible colleges from citation cartels ex post from looking at metadata networks of publication and find that a problem. And we have a discourse on that in the bibliometrics community. I will show you some short quotes how people talk about those citation cartels. So e.g. Davis in 2012 said: “George Franck warned us on the possibility of citation cartels – groups of editors and journals working together for mutual benefit.” So we have heard about their journal impact factors, so they... it’s believed that editors talk to each other: “Hey you cite my journal, I cite your journal, and we both will boost our impact factors.” So we have people trying to detect those cartels, and Mongeon et al. wrote that: “We have little knowledge about the phenomenon itself and about where to draw the line between acceptable and unacceptable behavior.” So we are having like moral discussions, about research ethics. And also we find discussions about the fairness of the impact factors. So Yang et al. wrote: “Disingenuously manipulating impact factor is the significant way to harm the fairness of the impact factor.” And that’s a very interesting thing I think, because why should an indicator be fair? So the... To believe that we have a fair measurement of scientific quality relevance and rigor in one single like number, like their journal impact factor, is not a small thing to say. And also we have a call for detection and punishment. So Davis also wrote: “If disciplinary norms and decorum cannot keep this kind of behavior at bay, the threat of being delisted from the JCR may be necessary.” So we find the moral concerns on right and wrong. We find the evocation of the fairness of indicators and we find the call for detection and punishment. When I first heard about that phenomenon of citation cartels which is believed to exist, I had something in mind which sounded... or it sounded like familiar to me. Because we have a similar information retrieval discourse or a discourse about ranking and power in a different area of society: in search engine optimization. So I found a quote by Page et al., who developed the PageRank algorithm – Google’s ranking algorithm – in 1999, which has changed since that a lot. But they wrote also a paper about the social implications of the information retrieval by the PageRank as an indicator. And wrote that: “These types of personalized PageRanks are virtually immune to manipulation by commercial interests. ... For example fast updating of documents is a very desirable feature, but it is abused by people who want to manipulate the results of the search engine.” And that was important to me to read because we also have like a narration of abuse, of manipulation, the perception that that might be fair, so we have a fair indicator and people try to betray it. And then we had in the early 2000s, I recall having a private website with a public guest book and getting link spam from people who wanted to boost their Google PageRanks, and shortly afterwards Google decided to punish link spam in their ranking algorithm. And then I got lots of emails of people saying: “Please delete my post from your guestbook because Google’s going to punish me for that.” We may say that this search engine optimization discussion is now somehow settled and it’s accepted that Google's ranking is useful. They have a secret algorithm, but it works and that is why it’s widely used. Although that journal impact factor seems to be transparent it’s basically the same thing that it's accepted to be useful and thus it's widely used. So the journal impact factor, the SCI and the like. We have another analogy so that Google decides which SEO behavior is regarded acceptable and punishes those who act against the rules and thus holds an enormous amount of power, which has lots of implications and led to the spreading of content management systems, for example, with search engine optimization plugins etc. We also have this power concentration in the hands of Clarivate (formerly ThomsonReuters) who host the database for the general impact factor. And they decide on who’s going to be indexed in those journal citation records and how is the algorithm, in detail, implemented in their databases. So we have this power concentration there too, and I think if we think about this analogy we might come to interesting thoughts but our time is running out so we are going to give a take-home message. Tl;dr, we find that the scientific community reacts with codes of conduct to a problem which is believed to exist. The strategic citation – we have database providers which react with sanctions so people are delisted from the journal citation records or journals are delisted from the journal citation records to punish them for citation stacking. And we have researchers and publishers who adapt their publication strategies in reaction to this perceived algorithmic power. But if we want to understand this as a problem we don’t have to only react to the algorithm but we have to address the power structures. Who holds these instruments in in their hands? If we talk about bibliometrics as an instrument and we should not only blame the algorithm – so #dontblamethealgorithm. Thank you very much! applause Herald: Thank you to Franziska, Teresa and Judith, or in the reverse order. Thank you for shining a light on how science is actually seen in its publications. As I started off as well, it’s more about scratching each other a little bit. I have some questions here from the audience. This is Microphone 2, please! Mic2: Yes, thank you for this interesting talk. I have a question. You may be familiar with the term ‘measurement dysfunction’, that if you provide a worker with an incentive to do a good job based on some kind of metric then the worker will start optimizing for the metric instead of trying to do a good job, and this is kind of inevitable. So, don’t you see that maybe it could be treating the symptoms if we just react about code of conduct, tweaking algorithms or addressing power structures. But instead we need to remove the incentives that lead to this measurement dysfunction. Judith: I would refer to this phenomenon as “perverse learning” – learning for the grades you get but not for your intrinsic motivation to learn something. We observe that in the science system. But if we only adapt the algorithm, so take away the incentives, would be like you wouldn’t want to evaluate research at all which you can probably want to do. But to whom would you address this call or this demand, so “please do not have indicators” or… I give the question back to you. laughs Herald: Okay, questions from the audience out there on the Internet, please. Your mic is not working? Okay, then I go to Microphone 1, please Sir. Mic1: Yeah, I want to have a provocative thesis. I think the fundamental problem is not how these things are gamed but the fundamental problem is that if we think the impact factor is a useful measurement for the quality of science. Because I think it’s just not. applause Judith: Ahm.. I.. Mic 1: I guess that was obvious... Judith: Yeah, I would not say that the journal impact factor is a measurement of scientific quality because no one has like a definition of scientific quality. So what I can observe is only people believe this journal impact factor to reflect some quality. Maybe they are chasing a ghost but I… whether that’s a valid measure is not so important to me, even if it were a relevant or a valid measure, it would concern me how it affects science. Herald: Okay, question from Microphone 3 there. Please. Mic3: Thanks for the interesting talk. I have a question about the 5,000 authors paper. Was that same paper published five thousand times or was it one paper with ten page title page? Franziska: No, it was one paper ... ... counting more than 7,000 words. And the authorship, so authors and co-authors, were more than 5,000. Mic3: Isn’t it obvious that this is a fake? Franziska: Well that’s what I meant earlier when saying, you have to see this within its context. So physicists are working with this with Atlas, this detective system. As there were some physicists in the audience they probably do know how this works. I do not. But as they claim it’s so much work to work with this, and it, as I said, requires so high maintenance it’s... They obviously have yeah... Mic3: So everybody who contributed was listed? Judith: Exactly, that’s it. And if this is ethically correct or not, well, this is something which needs to be discussed, right? This is why we have this talk, as we want to make this transparent, and contribute it to an open discussion. Herald: Okay, I’m sorry guys. I have to cut off here because our emission out there in space is coming to an end. I suggest that you guys find each other somewhere, maybe in the tea house or... Judith: Sure. We are around, we are here. Herald: You are around. I would love to have lots of applause for these ladies, for it really lights on how these algorithms not or are working. Thank you very much! Judith: Thank you! postroll music subtitles created by c3subtitles.de in the year 2018