As scholarship becomes increasingly globalized, bibliometric systems for quantifying research productivity have become increasingly relevant to academia (Gingras, 2014; Hicks et al., 2015; Lillis & Curry, 2010; Pansu et al., 2013). Bibliometric indicators are used to convert information about research activity (primarily publications and citations) into numbers that, in their apparent neutrality, seem to transcend linguistic and cultural (including disciplinary) boundaries. Developed as a way to study academic publication and citation patterns statistically, bibliometrics were originally used mostly for research purposes – to substantiate claims about who produces what and under which circumstances (De Bellis, 2014). Today, however, bibliometrics are most familiar to scholars as evaluative devices (see, e.g. Pansu et al., 2013). Bibliometric indicators are used to assess research performance, not only at the institutional level (to distribute funding to universities, rank journals and so on) but also increasingly at the individual level in the context of hiring or promotion decisions (Gingras, 2014). For those asked to evaluate the work of scholars from different countries, disciplines or contexts, the promise of simple indicators that would enable comparisons based on seemingly objective metrics – applicable beyond disciplinary and linguistic boundaries – can be enticing. The question is whether current bibliometric indicators are as neutral as they seem – whether they satisfactorily ‘quantify’ scholarly activity and whether the resulting number means the same thing to scholars across different settings (Gingras, 2014).

The aim of this chapter is to look at bibliometrics as a specific instance of quantification and thus – as with any other form of quantification – as a form of governing things and people. The politics of bibliometrics deserve to be unpacked because even with the best intentions, developers of bibliometric indicators must make non-trivial decisions about how to measure things that are notoriously difficult to quantify (De Bellis, 2014). These decisions require answering important questions such as: What does ‘productivity’ entail? What should count and how should it be counted? (Gingras, 2014).