Abstract
This chapter extends the analysis of the previous chapter to the role of metrics in political practice, using the U.S. standard graduation rate metric as a case. I argue that information is best understood as a process of communication in which observation is encoded into data through the translation regime and then decoded into metrics which are then institutionalized in political processes. In both processes, political factors are prominent, making metrics a political outcome at the least. I go further, however, showing that metrics play important distributive roles in politics, allocating material and moral goods as well as the conditions of political power. Metrics also exercise political control directly, working much like administrative procedures to select favored outcomes without direct legislative intervention and building the capacity of the state to exercise control over policy areas.
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Notes
- 1.
For the purpose of this chapter, I will use “data” to refer to a representation of a purportedly unified construct and “metric” to refer to combinations of data points that provide a standard for evaluation. Paradigmatically, the number of students in the IPEDS Graduation Rate Survey cohort is data. The GRS150 graduation rate is a metric composed of three data points (the number of students in the GRS cohort, the number of graduates from that cohort, and the number of students excluded from the cohort) and a mathematical transformation of those data. Metrics might also include growth rates in a single type of data over time, transformations of other metrics, or comparisons to other data or to a benchmark value. They would rarely, if ever, be single data points themselves, as such provide no basis for evaluation; likely, there is an implicit relationship to other data in metrics that are so defined.
- 2.
In U.S. usage there is no precise, formal distinction between colleges and universities. Institutions that use “college” in their names are more typically either smaller “liberal arts” colleges that originated as institutions to train primary and secondary teachers or 2-year community colleges oriented toward vocational training and programs that transfer to institutions offering bachelor’s degrees. They are almost—but not quite—always purely undergraduate institutions. Universities are typically larger than liberal arts colleges (though community colleges can span the entire range of institutional size) and usually (but, again, not always) offer graduate programs of widely varying scope. The distinctions, however, are primarily nominal and not analytically useful due to the vast overlap in institutional characteristics. This chapter will thus conform to the more common international usage, using “university” to refer to all U.S. institutions offering post-secondary degrees.
- 3.
The Department of Education is barred from collecting student unit record data under sec. 113 of the Higher Education Opportunity Act of 2008. While efforts to change this are very nearly constant, none have yet come close to success.
- 4.
This case is discussed in more detail in Sect. 6.1.1.
- 5.
And thus to treat themselves as a non-gendered single author on its web site, e.g., “McNollgast is most well-known for its early articles that helped introduce positive political theory (PPT) into the study of administrative law” (Weingast 2013).
- 6.
One is tempted to say “always-already,” as is the current fashion. But that is not quite right. The central point of the continental formulation is to suggest a point after which it is impossible to conceive of the time before: Humans are always-already linguistic because the only way we can frame a time before language is to use language. We are thus already linguistic in the present (having acquired language at some point in the past and not needing to do so now), but that acquisition must appear to have always been the case because there is no possibility of understanding what was before. That is not how data is political. Data has not become politicized in such a way that we can never again understand its pre-political state. Data is political from the moment it comes into existence. Data is thus always-but-not-already political because it is impossible to create data at any time without engaging in politics.
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Johnson, J.A. (2018). The Political Life of Metrics. In: Toward Information Justice. Public Administration and Information Technology, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-319-70894-2_4
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