PLoS Journals – measuring impact where it matters

In 2009, in this online world, how do most scientists and medics find the articles they need to read? The answer for the content published by PLoS (and no doubt by many other publishers) is via one of the now ubiquitous search engines, be it one that only searches the scientific literature, or more likely, one that searches the entire web. Given that readers tend to navigate directly to the articles that are relevant to them, regardless of the journal they were published in, why then do researchers and their paymasters remain wedded to assessing individual articles by using a metric (the impact factor) that attempts to measure the average citations to a whole journal? We’d argue that it’s primarily because there has been no strong alternative. But now alternatives are beginning to emerge.

A few months ago, PLoS initiated a program to provide a series of metrics on the individual articles published in all the PLoS Journals. You can see some examples here, here, here and here. There are two complementary benefits to the new approach.

First, we are focusing on articles rather than journals. The dominant paradigm for judging the worth of an article is to rely on the name and the impact factor of the journal in which the work is published. But it’s well known that there is a strong skew in the distribution of citations within a journal – typically, around 80% of the citations accrue to 20% of the articles. So the impact factor is a very poor predictor of how many citations any individual article will obtain, and in any case, journal editors and peer reviewers don’t always make the right decision. Indicators at the article level circumvent these limitations, allowing articles to be judged on their own scientific merits.

Second, we are not confining article-level metrics to a single indicator. As summarized by Michael Jensen, and discussed by many others including recently over at the Scholarly Kitchen, there’s a lot more to scientific impact than citations in the selection of journals covered by the Web of Science – the proprietary source of data that provides the impact factor calculation. Citations can be counted more broadly, along with web usage, blog and media coverage, social bookmarks, expert/community comments and ratings, and so on. Our own efforts are so far confined to citations (as measured by Scopus and PubMed Central), social bookmarks (as made by users of Connotea and CiteULike), and blog coverage (as recorded by Bloglines, Postgenomic and Nature Blogs), and these metrics will be improved and expanded over the coming months. The good news is that many of these indicators can be collated automatically, using openly available web tools that constantly update information on the article itself.

The presentation of a comprehensive array of this data is an enticing prospect. When an article has been published, we have tended to regard that as the end of the story (barring corrections or the occasional retraction). But if, as frequently happens, a very good article has been published in a specialist journal after being rejected from a highly selective one, it would be great to indicate to a user that this article is actually looking pretty significant, and show how its influence develops over the months and years.

Rather than basing judgments on the importance of research on the opinions of two or three reviewers and editors, article-level metrics will attempt to capture the actions and opinions of entire communities of readers to give a rich and sophisticated picture of research impact that will be helpful to authors and readers alike. Readers may then frame that picture in the context of their particular field and their own work.

To realize the vision for article-level metrics there are still some significant hurdles to clear: it won’t be enough simply to provide indicators without some context or guidance on how to interpret them; some indicators (particularly citations) take months to build up limiting their value as early indicators of impact; and standards will need to be developed so that the indicators are reliable and as free as possible from gaming and manipulation.

A clear editorial selection process will always have a place before publication in a scholarly journal. But a reduction in the reliance on the impact factor for so many aspects of research assessment could be massively liberating. PLoS Medicine, to cite an example close to home, has recently restated its mission – focusing on the diseases and risk factors that have the most profound impacts on global health. By carefully selecting articles that are likely to have the biggest influence on global health and using innovative and diverse approaches to assess and indicate that influence, PLoS Medicine will be a greater force, regardless of how many citations an average article accrues

Looking towards other modes of publishing, PLoS ONE is predicated on the notion that judgements about impact and relevance can be left almost entirely to the period after publication. By peer-reviewing submissions purely for scientific rigour, ethical conduct and proper reporting before publication, articles can be assessed and published rapidly. Once articles have joined the published literature, the impact and relevance of the article can then be determined on the basis of the activity of the research community as a whole. Article-level metrics and indicators, along with other post-publication features are part and parcel of the PLoS ONE approach, and could help readers to filter and sort literature after it is published. Ultimately, the aim of adding value to articles after publication is to improve the whole process of scientific communication and accelerate research progress itself. You can read more about article-level metrics in the context of PLoS ONE, and a talk is also available online from Pete Binfield (Managing Editor of PLoS ONE).

Article-level metrics and indicators will become powerful additions to the tools for the assessment and filtering of research outputs, and we look forward to working with the research community, publishers, funders and institutions to develop and hone these ideas. As for the impact factor, the 2008 numbers were released last month. But rather than updating the PLoS Journal sites with the new numbers, we’ve decided to stop promoting journal impact factors on our sites all together. It’s time to move on, and focus efforts on more sophisticated, flexible and meaningful measures.

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3 Responses to PLoS Journals – measuring impact where it matters

  1. Pingback: NIF Blog » Blog Archive » A Call to Science Bloggers

  2. Carlo Riccardi says:

    The IF is not appropriate to evaluate the value of an article. Citations over the time are more appropriate for an individual article evaluation. Notably, the entire number of citations should be considered and not the number of citations in a certain period of time: citation of an article published many years ago may have more value than citation of recently published articles.

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  3. Patterson, Mark (2009) PLoS Journals – measuring impact where it matters writes:

    “[R]eaders tend to navigate directly to the articles that are relevant to them, regardless of the journal they were published in… [T]here is a strong skew in the distribution of citations within a journal – typically, around 80% of the citations accrue to 20% of the articles… [W]hy then do researchers and their paymasters remain wedded to assessing individual articles by using a metric (the impact factor) that attempts to measure the average citations to a whole journal?

    “We’d argue that it’s primarily because there has been no strong alternative. But now alternatives are beginning to emerge… focusing on articles rather than journals… [and] not confining article-level metrics to a single indicator… Citations can be counted more broadly, along with web usage, blog and media coverage, social bookmarks, expert/community comments and ratings, and so on…

    “[J]udgements about impact and relevance can be left almost entirely to the period after publication. By peer-reviewing submissions purely for scientific rigour, ethical conduct and proper reporting before publication, articles can be assessed and published rapidly. Once articles have joined the published literature, the impact and relevance of the article can then be determined on the basis of the activity of the research community as a whole… [through] [a]rticle-level metrics and indicators…”

    Merits of Metrics. Of course direct article and author citation counts are infinitely preferable to — and more informative than — just a journal average (the journal “impact factor”).

    And yes, multiple postpublication metrics will be a great help in navigating, evaluating and analyzing research influence, importance and impact.

    But it is a great mistake to imagine that this implies that peer review can now be done on just a generic “pass/fail” basis.

    Purpose of Peer Review. Not only is peer review dynamic and interactive — improving papers before approving them for publication — but the planet’s 25,000 peer-reviewed journals differ not only in the subject matter they cover, but also, within a given subject matter, they differ (often quite substantially) in their respective quality standards and criteria.

    It is extremely unrealistic (and would be highly dysfunctional, if it were ever made to come true) to suppose that these 25,000 journals are (or ought to be) flattened to provide a 0/1 pass/fail decision on publishability at some generic adequacy level, common to all refereed research.

    Pass/Fail Versus Letter-Grades. Nor is it just a matter of switching all journals from assigning a generic pass/fail grade to assigning its own letter grade (A-, B+, etc.), despite the fact that that is effectively what the current system of multiple, independent peer-reviewed journals provides. For not only do journal peer-review standards and criteria differ, but the expertise of their respective “peers” differs too. Better journals have better and more exacting referees, exercising more rigorous peer review. (So the 25,000 peer-reviewed journals today cannot be thought of as one generic peer-review filter that accepts papers for publication in each field with grades between A+; rather there are A+ journals, B- journals, etc.: each established journal has its own independent standards, to which submissions are answerable)

    Track Records and Quality Standards. And users know all this, from the established track records of the journals they consult as readers and publish in as authors. Whether or not we like to put it that way, this all boils down to selectivity across a gaussian distribution of research quality in each field. There are highly selective journals, that accept only the very best papers — and even those often only after several rounds of rigorous refereeing, revision and re-refereeing. And there are less selective journals, that impose less exacting standards — all the way down to the fuzzy pass/fail threshold that distinguishes “refereed” journals from journals whose standards are so low that they are virtually vanity-press journals.

    Supplement Versus Substitute. This difference (and independence) among journals in terms of their quality standards is essential if peer-review is to serve as the quality enhancer and filter that it is intended to be. Of course the system is imperfect, and, for just that reason alone (amongst many others) a rich diversity of post-publication metrics are an invaluable supplement to peer review. But they are certainly no substitute for pre-publication peer review, or, most importantly, its quality triage.

    Quality Distribution. So much research is published daily in most fields that on the basis of a generic 0/1 quality threshold, researchers simply cannot decide rationally or reliably what new research is worth the time and investment to read, use and try to build upon. Researchers and their work differ in quality too, and they are entitled to know a priori, as they do now, whether or not a newly published work has made the highest quality cut, rather than merely that it has met some default standards, after which users must wait for the multiple post-publication metrics to accumulate across time in order to be able to have a more nuanced quality assessment.

    Rejection Rates. More nuanced sorting of new research is precisely what peer review is about, and for, and especially at the highest quality levels. Although authors (knowing the quality track-records of their journals) mostly self-select, submitting their papers to journals whose standards are roughly commensurate with their quality, the underlying correlate of a journal’s refereeing quality standards is basically their relative rejection rate: What percentage of annual papers in their designated subject matter would meet their standards (if all were submitted to that journal, and the only constraint on acceptance were the quality level of the article, not how many articles the journal could manage to referee and publish per year)?

    Quality Ranges. This independent standard-setting by journals effectively ranges the 25,000 titles along a rough letter-grade continuum within each field, and their “grades” are roughly known by authors and users, from the journals’ track-records for quality.

    Quality Differential. Making peer review generic and entrusting the rest to post-publication metrics would wipe out that differential quality information for new research, and force researchers at all levels to risk pot-luck with newly published research (until and unless enough time has elapsed to sort out the rest of the quality variance with post-publication metrics). Among other things, this would effectively slow down instead of speeding up research progress.

    Turn-Around Time. Of course pre-publication peer review takes time too; but if its result is that it pre-sorts the quality of new publications in terms of known, reliable letter-grade standards (the journals’ names and track-records), then it’s time well spent. Offloading that dynamic pre-filtering function onto post-publication metrics, no matter how rich and plural, would greatly handicap research usability and progress, and especially at its all-important highest quality levels.

    More Value From Post-Publication Metrics Does Not Entail Less Value From Pre-Publication Peer Review. It would be ironic if today’s eminently valid and timely call for a wide and rich variety of post-publication metrics — in place of just the unitary journal average (the “journal impact factor”) — were coupled with an ill-considered call for collapsing the planet’s wide and rich variety of peer-reviewed journals and their respective independent, established quality levels onto some sort of global, generic pass/fail system.

    Differential Quality Tags. There is an idea afoot that peer review is just some sort of generic pass/fail grade for “publishability,” and that the rest is a matter of post-publication evaluation. I think this is incorrect, and represents a misunderstanding of the actual function that peer review is currently performing. It is not a 0/1, publishable/unpublishable threshold. There are many different quality levels, and they get more exacting and selective in the higher quality journals (which also have higher-quality and more exacting referees and refereeing). Users need these differential quality tags when they are trying to decide whether newly published work is worth taking the time to ready and making the effort and risk to try to build upon (at the quality level of their own work).

    User/Author/Referee Experience. I think both authors and users have a good idea of the quality levels of the journals in their fields — not from the journals’ impact factors, but from their content, and their track-records for content. As users, researchers read articles in their journals; as authors they write for those journals, and revise for their referees; and as referees they referee for them. They know that all journals are not equal, and that “peer-reviewed” can be done at a whole range of quality levels.

    Metrics As Substitutes for User/Author/Referee Experience? Is there any substitute for this direct experience with journals (as users, authors and referees) in order to know what their peer-reviewing standards and quality level are? There is nothing yet, and no one can say yet whether there will ever be metrics as accurate as having read, written and refereed for the journals in question. Metrics might eventually provide an approximation, though we don’t yet know how close, and of course they only come after publication (well after).

    Quality Lapses? Journal track records, user experiences, and peer review itself are certainly not infallible either, however; the usually-higher-quality journals may occasionally publish a lower-quality article, and vice versa. But on average, the quality of the current articles should correlate well with the quality of past articles. Whether judgements of quality from direct experience (as user/author/referee) will ever be matched or beaten by multiple metrics, I cannot say, but I am pretty sure they are not matched or beaten by the journal impact factor.

    Regression on the Generic Mean? And even if multiple metrics do become as good a joint predictor of journal article quality as user experience, it does not follow that peer-review can then be reduced to generic pass/fail, with the rest sorted by metrics, because (1) metrics are journal-level, not article-level (though they can also be author-level) and, more important still, (2) if journal-differences are flattened to generic peer review, entrusting the rest to metrics, then the quality of articles themselves will fall, as rigorous peer review does not just just assign articles a differential grade (via the journal’s name and track-record), but it improves them, through revision and re-refereeing. More generic 0/1 peer review, with less individual quality variation among journals, would just generate quality regression on the mean.

    REFERENCES

    Bollen J, Van de Sompel H, Hagberg A, Chute R (2009) A Principal Component Analysis of 39 Scientific Impact Measures. PLoS ONE 4(6): e6022. doi:10.1371/journal.pone.0006022

    Brody, T., Harnad, S. and Carr, L. (2006) . Journal of the American Association for Information Science and Technology (JASIST) 57(8) pp. 1060-1072.

    Garfield, E., (1955) Citation Indexes for Science: A New Dimension in Documentation through Association of Ideas. Science 122: 108-111

    Harnad, S. (1979) Creative disagreement. The Sciences 19: 18 – 20.

    Harnad, S. (ed.) (1982) Peer commentary on peer review: A case study in scientific quality control, New York: Cambridge University Press.

    Harnad, S. (1984) Commentaries, opinions and the growth of scientific knowledge. American Psychologist 39: 1497 – 1498.

    Harnad, Stevan (1985) Rational disagreement in peer review. Science, Technology and Human Values, 10 p.55-62.

    Harnad, S. (1990) Scholarly Skywriting and the Prepublication Continuum of Scientific Inquiry Psychological Science 1: 342 – 343 (reprinted in Current Contents 45: 9-13, November 11 1991).

    Harnad, S. (1986) Policing the Paper Chase. (Review of S. Lock, A difficult balance: Peer review in biomedical publication.) Nature 322: 24 – 5.

    Harnad, S. (1996) Implementing Peer Review on the Net: Scientific Quality Control in Scholarly Electronic Journals. In: Peek, R. & Newby, G. (Eds.) Scholarly Publishing: The Electronic Frontier. Cambridge MA: MIT Press. Pp 103-118.

    Harnad, S. (1997) Learned Inquiry and the Net: The Role of Peer Review, Peer Commentary and Copyright. Learned Publishing 11(4) 283-292.

    Harnad, S. (1998/2000/2004) The invisible hand of peer review. Nature [online] (5 Nov. 1998), Exploit Interactive 5 (2000): and in Shatz, B. (2004) (ed.) Peer Review: A Critical Inquiry. Rowland & Littlefield. Pp. 235-242.

    Harnad, S. (2008) Validating Research Performance Metrics Against Peer Rankings. Ethics in Science and Environmental Politics 8 (11) Special Issue: The Use And Misuse Of Bibliometric Indices In Evaluating Scholarly Performance

    Harnad, S. (2009) Open Access Scientometrics and the UK Research Assessment Exercise. Scientometrics 79 (1)

    Shadbolt, N., Brody, T., Carr, L. and Harnad, S. (2006) The Open Research Web: A Preview of the Optimal and the Inevitable, in Jacobs, N., Eds. Open Access: Key Strategic, Technical and Economic Aspects. Chandos.

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