This originally appeared on the ACRL TechConnect blog.
Bibliometrics– used here to mean statistical analyses of the output and citation of periodical literature–is a huge and central field of library and information science. In this post, I want to address the general controversy surrounding these metrics when evaluating scholarship and introduce the emerging alternative metrics (often called altmetrics) that aim to address some of these controversies and how these can be used in libraries. Librarians are increasingly becoming focused on the publishing side of the scholarly communication cycle, as well as supporting faculty in new ways (see, for instance, David Lankes’s thought experiment of the tenure librarian). What is the reasonable approach for technology-focused academic librarians to these issues? And what tools exist to help?
There have been many articles and blog posts expressing frustration with the practice of using journal impact factors for judging the quality of a journal or an individual researcher (see especially Seglen). One vivid illustration of this frustration is in a recent blog post by Stephen Curry titled “Sick of Impact Factors”. Librarians have long used journal impact factors in making purchasing decisions, which is one of the less controversial uses of these metrics 1 The essential message of all of this research about impact factors is that traditional methods of counting citations or determining journal impact do not answer questions about what articles are influential and how individual researchers contribute to the academy. For individual researchers looking to make a case for promotion and tenure, questions of use of metrics can be all or nothing propositions–hence the slightly hysterical edge in some of the literature. Librarians, too, have become frustrated with attempting to prove the return on investment for decisions–see “How ROI Killed the Academic Library”–going by metrics alone potentially makes the tools available to researchers more homogeneous and ignores niches. As the alt metrics manifesto suggests, the traditional “filters” in scholarly communication of peer review, citation metrics, and journal impact factors are becoming obsolete in their current forms.
It would be of interest to determine, if possible, the part which men of different calibre [sic] contribute to the progress of science.
Alfred Lotka (a statistician at the Metropolitan Life Insurance Company, famous for his work in demography) wrote these words in reference to his 1926 statistical analysis of the journal output of chemists 2 Given the tools available at the time, it was a fairly limited sample size, looking at just the first two letters of an author index for the period of 16 years compared with a slim 100 page volume of important works “from the beginning of history to 1900.” His analysis showed that the more articles published in a field, the less likely it is for an individual author to publish more than one article. As Per Seglen puts it, this showed the “skewness” of science 3
The original journal impact factor was developed by Garfield in the 1970s, and used the “mean number of citations to articles published in two preceding years” 4. Quite clearly, this is supposed to measure the general amount that a journal was cited, and hence a guide to how likely a researcher was to read and immediately find useful the body of work in this journal in his or her own work. This is helpful for librarians trying to make decisions about how to stretch a budget, but the literature has not found that a journal’s impact has much to do with an individual article’s citedness and usefulness 5 As one researcher suggests, using it for anything other than its intended original use constitutes pseudoscience 6 Another issue with which those at smaller institutions are very familiar is the cost of accessing traditional metrics. The major resources that provide these are Thomson Reuters’ Journal Citation Reports and Web of Science, and Elsevier’s Scopus, and both are outside the price range of many schools.
Metrics that attempt to remedy some of these difficulties have been developed. At the journal level, the Eigenfactor® and Article Influence Score™ use network theory to estimate “the percentage of time that library users spend with that journal”, and the Article Influence Score tracks the influence of the journal over five years. 7. At the researcher level, the h-index tracks the impact of specific researchers (it was developed with physicists in mind). The h-index takes into account the number of papers the researcher has published in how much time when looking at citations. 8
These are included under the rubric of alternative metrics since they are an alternative to the JCR, but rely on citations in traditional academic journals, something which the “altmetric” movement wants to move beyond.
In this discussion of alt metrics I will be referring to the arguments and tools suggested by Altmetrics.org. In the alt metrics manifesto, Priem et al. point to several manifestations of scholarly communication that are unlike traditional article publications, including raw data, “nanopublication”, and self-publishing via social media (which was predicted as so-called “scholarly skywriting” at the dawn of the World Wide Web 9). Combined with sharing of traditional articles more readily due to open access journals and social media, these all create new possibilities for indicating impact. Yet the manifesto also cautions that we must be sure that the numbers which alt metrics collect “really reflect impact, or just empty buzz.” The research done so far is equally cautious. A 2011 study suggests that tweets about articles (tweetations) do correlate with citations but that we cannot say that number of tweets about an article really measures the impact. 10
A criticism expressed in the media about alt metrics is that alternative metrics are no more likely to be able to judge the quality or true impact of a scientific paper than traditional metrics. 11 As Per Seglen noted in 1992, “Once the field boundaries are broken there is virtually no limit to the number of citations an article may accrue.” 12 So an article that is interdisciplinary in nature is likely to do far better in the alternative metrics realm than a specialized article in a discipline that still may be very important. Mendeleley’s list of top research papers demonstrates this–many (though not all) the top articles are about scientific publication in general rather than about specific scientific results.
What can librarians use now?
Librarians are used to questions like “What is the impact factor of Journal X?” For librarians lucky enough to have access to Journal Citation Reports, this is a matter of looking up the journal and reporting the score. They could answer “How many times has my article been cited?” in Web of Science or Scopus using some care in looking for typos. Alt metrics, however, remind us that these easy answers are not telling the whole story. So what should librarians be doing?
One thing that librarians can start doing is helping their campus community get signed up for the many different services that will promote their research and provide article level citation information. Below are listed a small number (there are certainly others out there) of services that you may want to consider using yourself or having your campus community use. Some, like PubMed, won’t be relevant to all disciplines. Altmetrics.org lists several tools beyond what is listed below to provide additional ideas.
- Google Scholar Metrics and Google Scholar Citations (personal research metrics)
- PubMed My Bibliography
- ORCID: Create a unique researcher ID.
These tools offer various methods for sharing. PubMed allows one to embed “My Bibliography” in a webpage, as well as to create delegates who can help curate the bibliography. A developer can use the APIs provided by some of these services to embed data for individuals or institutions on a library website or institutional repository. ImpactStory has an API that makes it relatively easy to embed data for individuals or institutions on a library website or institutional repository. Altmetric.com also has an API that is free for non-commercial use. Mendeley has many helpful apps that integrate with popular content management systems.
Since this is such a new field, it’s a great time to get involved. Altmetrics.org held a hackathon in November 2012 and has a Google Doc with the ideas for the hackathon. This is an interesting overview of what is going on with open source hacking on alt metrics.
The altmetrics manifesto program calls for a complete overhaul of scholarly communication–alternative research metrics are just a part of their critique. And yet, for librarians trying to help researchers, they are often the main concern. While science in general calls for a change to the use of these metrics, we can help to shape the discussion through educating and using alternative metrics.
Works Cited and Suggestions for Further Reading
- Jerome K. Vanclay, “Impact Factor: Outdated Artefact or Stepping-stone to Journal Certification?” Scientometrics 92 (2) (2011): 212. ↩
- Alfred Lotka, “The Frequency Distribution of Scientific Productivity.” Journal of the Washington Academy of Sciences 26 (12) (1926)): 317. ↩
- Per Seglen, “The Skewness of Science.” Journal of the American Society for Information Science 43 (9) (1992): 628. ↩
- Vanclay, 212. ↩
- Per Seglen, “Causal Relationship Between Article Citedness and Journal Impact.” Journal of the American Society for Information Science 45 (1) (1994): 1-11. ↩
- Vanclay, 211. ↩
- “Methods”, Eigenfactor.org, 2012. ↩
- J.E. Hirsch, “An Index to Quantify an Individual’s Scientific Research Output.” Proceedings of the National Academy of Sciences of the United States of America 102, no. 46 (2005): 16569–16572. ↩
- Blaise Cronin and Kara Overfelt, “E-Journals and Tenure.” Journal of the American Society for Information Science 46 (9) (1995): 700. ↩
- Gunther Eysenbach, “Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact.” Journal Of Medical Internet Research 13 (4) (2011): e123. ↩
- see in particular Jump. ↩
- Seglen, 637. ↩