I intended the last entry (30 May 2022) to trouble a common conceptualization of “data” as straightforward facts and information that can be marshalled to prove all sorts of things. I argued that the destabilization of this conceptualization can be accomplished by recognizing both how “data” are social products and how they participate in governing practices that produce “realities”. 

Such a stance recognizes “data” as necessarily political – embroiled in social relations. Mol (2002: 155, emphasis in original) says as much in her insistence that “Methods are not a way of opening a window on the world, but a way of interfering with it. They act, they mediate between an object and its representations”. She follows up this proposition with this advice to PhD students: we ought to consider “not what we want to know”, but “what we want to do”. As Mol (2002: 151) puts it, “veracity is not the point. Instead, it is interference”. 

Springgay and Truman (2018: 206) develop a similar argument. They question “a reliance on data modelled on knowability and visibility”. The point of research is not “reporting on the world” but “doing rather than meaning making”:

“What has become increasingly clear is that rather than trying to collect data or represent an objective reality (methods that privilege the human and treat data as existing phenomena), we need to think about inventive practices that ‘intervene, disturb, intensify or provoke a heightened sense of the potentiality of the present’ (Sheller, 2014, p. 134)”. 

This approach requires a different orientation to methods” (Springgay and Truman 2018: 206) in which “particular (in)tensions need to be immanent to whatever method is used”. 

The form of this challenge to conventional research approaches needs to be elaborated. It is beyond uncommon for researchers to declare that they do not wish to produce knowledge. To embrace such a project puts students in a precarious position in relation to acceptance of their work. As Koro-Ljungberg and MacLure (2013: 219) describe, 

“the concept of data is generally treated as being unproblematic. Data are simply regarded as something we collect and analyze in order to arrive at research conclusions. Data has become a key element of one of the main grand narratives of research”.  

St Pierre (2016: 116) helpfully locates this stance on “data” within a history of logical positivism, which endorses an empiricism grounded in mathematics: 

“Logical empiricism strongly influenced natural science, social science, and philosophy in the United States until the 1960s when its premises were critiqued, for example, by interpretivism, social constructionism, critical theories, and the ‘posts’ [e.g. post-structuralism, post-humanism, etc.], and it fell out of favor”. 

This situation changed in the new century when “logical empiricism returned with a vengeance in U.S. education with a neopositivist description of scientifically based research in education (see St Pierre 2006)”. According to St Pierre this neopositivism now dominates educational research and practice, and many social science disciplines – including psychology, political science and economics.

St Pierre (2016: 111) identifies a second empiricism operating in conventional social science methods, an “empiricism of phenomenology” that privileges “experience as the primary source of and justification for knowledge”. Here empiricists “insist on facts supported by sense impressions or brute datum found through careful observation of experience (experiment) to justify knowledge claims”. 

We can see the playing out of these intellectual developments in the debates about quantitative versus qualitative research, and the frequent endorsement of “mixed methods” (Bryman 2006; see Research Hub entry 31 August 2021). Meanwhile, the contemporary focus on “evidence-based policy” and on “what works” signals the enduring strength of “scientifically based research” (Bacchi 2009: 252-255). Social sciences struggle for legitimacy in this climate, indicated I suggest in the metaphorical adoption of “data” to describe qualitative research materials (see previous entry). 

More recent post-qualitative research approaches bring a distinctive perspective to the issue of “data” and “authenticity”. These approaches challenge both a correspondence view of knowledge (see last entry) and the privileging of human actors in research practices. Springgay and Truman (2018: 206) draw links between post-qualitative arguments and the “new materialisms” in the determination to de-privilege human actors while incorporating an understanding of matter as “lively”. In post-qualitative inquiry there can be “no post qualitative data or methods of data collection or methods of data analysis”. For St Pierre (2016: 113), with Leonelli (2014: 1), “there is no such thing as direct inference from data”.

Because conventional humanist qualitative methodologies draw heavily on phenomenology and the humanist subject, and because their methods-driven approach also draws heavily on logical positivism, St Pierre concludes that they cannot accommodate the new post-qualitative perspective. On these grounds St Pierre (2016: 122) calls upon researchers to try to forget their training, to put “methodology aside” and, instead, to read “widely across philosophy, social theory, and the history of science and social science to find concepts that reorient thinking” (St Pierre 2019: 10).

In a previous entry (31 May 2019) I tried to take a tentative step away from the depoliticizing implications of this apparent abandonment of research methods. There I suggest that critical ethnography (see also entry 28 Feb 2019) and a determination to interrogate our concepts (self-problematization) create room to explore the possibility of adopting “a wide gamut of empirical techniques, as part of a commitment to selected political goals” (Bacchi and Goodwin 2016: 23; see Tamboukou and Ball 2003). I proceed below to make a similar case for engaging with (not “using”) “data”. To engage with data requires research attuned both to how data is produced and to how it is productive (constitutive), as argued in the previous entry (30 May 2022). I now proceed to draw attention to some innovative work that takes on these tasks.

Patti Lather (2013: 643) notes that the “age of big data” and the “march of quantification” are not going away. In this setting Geoffrey Bowker (2013: 170-171) insists that “If data are so central to our lives and our planet, then we need to understand just what they are and what they are doing”. To this end Cambrioso et al. (2014: 20) argue that “social scientists can enter into a reflexive relation with the entities they analyze”. To do this Leonelli (2017: 195) recommends that “we attend to the negotiations regarding what counts as data, for whom, when, where, why – and how this changes – and what is regarded as missing in such processes”.  

Fordyce and Jethani (2021) have begun to develop an analytic heuristic or tool to undertake this form of analysis. They start from the notion of “data provenance”, which attends to the origins of a piece of data and to the methodology by which it was produced. They proceed to ask additional questions about that data in a method they call “critical data provenance”. The additional questions address “who”, “what”, “when” and “how” data are produced and operate, producing a history (or genealogy) of datasets. Fordyce and Jethani (2021: 3) argue that this framework expands “beyond the technical to act as an analytical ethics tool for thinking about transactions in data and their consequences” (see also Beer 2016). 

QuantCrit, a methodological sub-field of Critical Race Studies in education, develops another “toolkit” to rethink and engage with “data” (Gillborn et al. 2018: 169). Researchers adopting this approach argue that “quantitative methods cannot be adopted for racial justice aims without an ontological reckoning that considers historical, social, political, and economic power relations” (Garcia et al., 2018: 149). To this end they develop a set of principles through which it becomes possible to re-imagine and “rectify” “data”:

       “(1) the centrality of racism

       (2) numbers are not neutral

(3) categories are neither ‘natural’” nor given: for ‘race’ read ‘racism’ 

(4) voice and insight: data cannot ‘speak for itself’

(5) using numbers for social justice” (Gillborn et al. 2018: 175).

Street et al. (2021: Abstract) usefully bring together QuantCrit principles, Indigenous Standpoint Theory and WPR “to elicit hidden assumptions within the use of statistics to measure the success of Indigenous higher education policies in the NT (Northern Territory)” in Australia.

My colleague Jennifer Bonham and I (2016) have taken on the specific task of how to deal with interviews as research material. We take up the challenge posed by St Pierre (2011: 620) in her critique of humanist research methods that rely on “a disentangled humanist self, individual, person”. To move past this obstacle to poststructural use of interview material we develop a methodology, referred to as Poststructural Interview Analysis (PIA), that treats “subjects” as provisional and in process. This approach allows us to shift the focus from trying to understand why the interviewee says what s/he says to the conditions that make it possible to say certain things, how those things are rendered “sayable” (Bacchi and Bonham 2016: 116). These “things said” are studied in terms of what they produce, or constitute, rather than in terms of what they mean (Bacchi and Bonham 2016: 118). Unsurprisingly, nowhere do Bonham and I refer to “interview data”.

In a Guest Editors’ Introduction to a Special Issue of Cultural Studies, Critical Methodologies – called simply “Data” – Koro-Ljungberg and MacLure (2013: 219) raise the possibility of “more active and significant roles for data”. In doing so they challenge the “subordinate or supplementary role” commonly assigned to “data”. That is, “data” seem to get their power from being seen as inert, as “evidence” that simply needs to be marshalled. Questioning the conceptualization of “data” as simply “facts” and “information”, therefore, opens up opportunities to find ways to deploy “data” creatively. 

Latour et al. (2012), for example, show that databases can play a role in questioning an agency/structure dichotomy, which has dominated and constrained sociopolitical thinking. In the place of that dichotomy they elaborate how the proliferation of data can point to the importance of “actors-networks” (2012: 607):

“It is because those databases provide the common experience to define the specificity of an actor as tantamount to expanding its network, that there is a chance to escape from choosing between what pertains to the individual and what pertains to the structure” (Latour et al. 2012: 612).

Along similar lines, Lather (2013: 639) shows that what “data” make available allows researchers to replace binaries with continuums and multiplicities. 

Latour el al. (2012: 612-613), of course, are well aware that data bases 

… are full of defects, that they themselves embody a rather crude definition of society, that they are marked by strong asymmetries of power, and above all that they mark only a passing moment in the traceability of the social connections.

Still, they believe “it would be a pity” to miss the opportunity to explore “another way to render the social sciences empirical and quantitative without losing their necessary stress on particulars”. 

Between 2006 and 2009 my colleague, Joan Eveline, and I worked closely with “data” in a project on gender analysis (see Bacchi and Eveline 2010). The reference group for the project, which included academics and policy workers, provided a space in which to discuss a variety of methods by which data on gender/gender relations may be collected. In an attempt to move away from the tendency simply to count “women” and “men”, and to try to capture the relational aspects of gendered interactions, the group agreed upon a distinction between “sex-disaggregated data” and “gender-disaggregated data”. 

While we found that all sorts of data could be useful in framing certain arguments (see Street et al. 2021:  Abstract), we also discovered that a focus on categorical distinctions (“women”, “men”), even within gender-disaggregated data, made it difficult to examine how those categories came to be (Bacchi 2017). In our efforts to bring attention to the ways in which policies constituted or “made” “women” and “men”, we recommended talking about gender as a verb or gerund (gender-ing) (Bacchi and Eveline 2010: 336). The goal here is to shift attention from gender as a fixed or essential characteristic of a person, to gender-ing as an attributional process (Bacchi 2001; see previous entry 30 May 2022). We hope this creative intervention provides a politically useful way to think about “gender” and “data”.

To engage with “data”, therefore, we need to pursue St Pierre’s (2019: 10) injunction to “find concepts that reorient thinking”. Deleuze and Guattari (1988: xii) compare a concept to a brick: it can be used to build a wall or it can be thrown through a window. To shatter a few windows, it is necessary to resist the common characterization of “data” as inert research material, passively waiting to be used. Specifically, we need to find ways to draw attention to two interconnected processes: how “data” are produced, and how they are productive (constitutive), making things come to be. In the process it becomes possible to explore new ways of deploying “data”. 


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