On exploding ‘evaluation machines’ and the construction of alt-metrics

The emergence of web-based ways to create and communicate new knowledge is affecting long-established scientific and scholarly research practices (cf. Borgman 2007; Wouters, Beaulieu, Scharnhorst, & Wyatt 2013). This move to the web is spawning a need for tools to track and measure a wide range of online communication forms and outputs. By now, there is a large differentiation in the kinds of social web tools (i.e. Mendeley, F1000,  Impact Story) and in the outputs they track (i.e. code, datasets, nanopublications, blogs). The expectations surrounding the explosion of tools and big ‘alt-metric’ data (Priem et al. 2010; Wouters & Costas 2012) marshal resources at various scales and gather highly diverse groups in pursuing new projects (cf. Brown & Michael 2003; Borup et al. 2006 in Beaulieu, de Rijcke & Van Heur 2013).

Today we submitted an abstract for a contribution to Big Data? Qualitative approaches to digital research (edited by Martin Hand & Sam Hillyard and contracted with Emerald). In the abstract we propose to zoom in on a specific set of expectations around altmetrics: Their alleged usefulness for research evaluation. Of particular interest to this volume is how altmetrics information is expected to enable a more comprehensive assessment of 1. social scientific outputs (under-represented in citation databases) and 2. wider types of output associated with societal relevance (not covered in citation analysis and allegedly more prevalent in the social sciences).

Our chapter we address a number of these expectations by analyzing 1) the discourse in the “altmetrics movement”, the expectations and promises formulated by key actors involved in “big data” (including commercial entities); and 2) the construction of these altmetric data and their alleged validity for research evaluation purposes. We will combine discourse analysis with bibliometric, webometric and altmetric methods in which both methods will also interrogate each others’ assumptions (Hicks & Potter 1991).

Our contribution will show, first of all, that altmetric data do not simply ‘represent’ other types of outputs; they also actively create a need for these types of information. These needs will have to be aligned with existing accountability regimes. Secondly, we will argue that researchers will develop forms of regulation that will partly be shaped by these new types of altmetric information. They are not passive recipients of research evaluation but play an active role in assessment contexts (cf. Aksnes & Rip 2009; Van Noorden 2010). Thirdly, we will show that the emergence of altmetric data for evaluation is another instance (following the creation of the citation indexes and the use of web data in assessments) of transposing traces of communication into a framework of evaluation and assessment (Dahler-Larsen 2012, 2013; Wouters 2014).

By making explicit what the implications are of the transfer of altmetric data from the framework of the communication of science to the framework of research evaluation, we aim to contribute to a better understanding of the complex dynamics in which new generation of researchers will have to work and be creative.

Aksnes, D. W., & Rip, A. (2009). Researchers’ perceptions of citations. Research Policy, 38(6), 895–905.

Beaulieu, A., van Heur, B. & de Rijcke, S. (2013). Authority and Expertise in New Sites of Knowledge Production. In A. Beaulieu, A. Scharnhorst, P. Wouters and S. Wyatt (Eds.), Virtual KnowledgeExperimenting in the Humanities and the Social Sciences. (pp. 25-56). MIT Press.

Borup, M, Brown, N., Konrad, K. & van Lente, H. 2006. “The sociology of expectations in science and technology.” Technology Analysis & Strategic Management 18 (3/4), 285-98.

Brown, N. & Michael, M. (2003). “A sociology of expectations: Retrospecting prospects and prospecting retrospects.” Technology Analysis & Strategic Management 15 (1), 3-18.

Costas, R., Zahedi, Z. & Wouters, P. (n.d.). Do ‘altmetrics’ correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective.

Dahler-Larsen, P. (2012). The Evaluation Society. Stanford University Press.

Dahler-Larsen, P. (2013). Constitutive Effects of Performance Indicators. Public Management Review, (May), 1–18.

Galligan, F., & Dyas-Correia, S. (2013). Altmetrics: Rethinking the Way We Measure. Serials Review, 39(1), 56–61.

Hicks, D., & Potter, J. (1991). Sociology of Scientific Knowledge: A Reflexive Citation Analysis of Science Disciplines and Disciplining Science. Social Studies of Science, 21(3), 459 –501.

Priem, J., Taraborelli, D., Groth, P., and Neylon, C. (2010a). Altmetrics: a manifesto. http://altmetrics.org/manifesto/

Van Noorden, R. (2010) “Metrics: A Profusion of Measures.” Nature, 465, 864–866.

Wouters, P., Costas, R. (2012). Users, narcissism and control: Tracking the impact of scholarly publications in the 21st century. Utrecht: SURF foundation.

Wouters, P. (2014). The Citation: From Culture to Infrastructure. In B. Cronin & C. R. Sugimoto (Eds.), Next Generation Metrics: Harnessing Multidimensional Indicators Of Scholarly Performance (Vol. 22, pp. 48–66). MIT Press.

Wouters, P., Beaulieu, A., Scharnhorst, A., & Wyatt, S. (eds.) (2013). Virtual Knowledge – Experimenting in the Humanities and the Social Sciences. MIT Press.


5 Responses to “On exploding ‘evaluation machines’ and the construction of alt-metrics”

  1. David Colquhoun Says:

    I hope you don’t miss the views of actual practising scientists on metrics -see http://www.dcscience.net/?p=6369
    There is a great danger that science will be corrupted by them, but that is brushed aside by bibliometricians.

  2. Zohreh Says:

    Nice work! just to mention that the name of Total Impact has been changed to Impact Story

  3. Jason Priem (@jasonpriem) Says:

    Cool, very excited to read this…hope you’ll ping us when it comes out! I love your three main points in the penultimate paragraph above…spot on, and not the kind of thing folks are talking about enough in this area right now, I think.

    You’re probably already familiar with Espeland’s “consilience” work [1]…this reminds me of some of the stuff she’s written about metrics and how they drive behavior as well as reflect it (to vastly oversimplify). I’d love to see her work discussed more in a biblio/web/altmetrics context. Of course we’re really conscious of this effect at Impactstory–in fact we’re counting on it. We want to help build and support a science reputation and evaluation culture that prioritizes open, engaged, reusable, and web-native science; the metrics are just an (essential) means to that end.

    [1] http://www.jstor.org/stable/10.1086/517897

  4. David Colquhoun Says:

    ” metrics … drive behavior as well as reflect it”

    That is precisely why they are a corrupting influence. I think most scientists just wish that you bean counters would get off our backs and leave us to get on with our jobs (which you evidently don’t understand very well).

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