A Genealogy of “Data”

Several months ago, I took some time to reflect on important debates about the place of “data” in research and in governing practices (29 April 2022, 30 May 2022, 29 June 2022). While updating my research for the previous entry on genealogy (30 July 2022), I encountered Colin Koopman’s 2019 book, entitled: How We Became Our Data: A Genealogy of the Informational Person. The timing could not have been better! Therefore, allow me to introduce Koopman’s argument to you as a way of illustrating the contribution that a genealogical sensibility makes to political thinking. 

I was happy to find clear links between Koopman’s argument and the position I developed in the previous entries on data: first, that data is produced and second, that data is productive. The first theme refers not to how specific bits of “data” are identified and agglomerated, but rather to how “data” become conceptualized as inert bits of information. The second theme highlights how “data” produce us as particular kinds of subject. Both themes are central to Koopman’s book. 

At the same time Koopman’s book offers an extremely useful example of genealogy at work. The key principles of a genealogical sensibility are clearly laid out, and I will use them as a guide to my comments in this entry. First, start your analysis from a problem or concern in the present. Second, consider that everything has a history. Third, identity the practices involved in the production of key concepts and the emergence of the particular concern. Fourth, consider the consequences. Fifth, consider the openings for change. 

Thinking back to the previous entry on genealogy, you may recall that by a “problem in the present” Foucault means some concern animating the researcher’s analysis. So, what are Koopman’s concerns about “data”? These are clearly laid out in the blurb describing How We Became Our Data (2019). 

Koopman asks: 

“How did information come to be so integral to what we can do? How did we become people who effortlessly present our lives in social media profiles and who are meticulously recorded in state surveillance dossiers and online marketing databases? What is the story behind data coming to matter so much to who we are?” 

Koopman then is concerned by the plain fact that we find ourselves enrolled in a thousand databases. He asks: “Who could you be without your data points? What could you do?” (Koopman 2019: Preface).

Recalling that a prime purpose of genealogy is to encourage political subjects to reflect on what it means to be human (see previous entry) Koopman develops the notion of “the informational person”. His book relates the story of the becoming of this form of subject.

Importantly, Koopman’s focus is on how this “informational subject” is constituted. That is, the argument is not that data are mere externalia 

“from which we might detach our truer selves as we please, but are rather constitutive parts of who we can be. Who we are is therefore deeply interactive with data. We are cyborgs who extend into our data”. (Koopman 2019: 8) 

In a review of Koopman, McWhorter (2020) elaborates:

“We are population and census data, certainly, but more intimately, we are our vital statistics, our credit reports, our personality inventories, our insurance policies, our educational records, our fitbit badges, our Facebook and dating app profiles”. 

 In a Symposium on Koopman’s book (https://syndicate.network/symposia/philosophy/how-we-became-our-data/), Smith drives home the point:

“Depending on our data, a financial transaction will be approved or blocked, a college admission will be accepted or rejected, entrance to a building will be granted or denied, a job application will be successful or unsuccessful, a border will be crossed or not”.

 Now, this “informational subject”, like all political subjects, has a history (see previous entry), and the genealogist’s task is to trace this history. The focus becomes the practices that led to the production of the “informational person”. These practices form the substance of Koopman’s book. To produce a genealogy of the “informational person” Koopman brings his “historical sense” to some selected early moments of the trends that disturb him.

Koopman locates the emergence of informational personhood in the period from the mid-1910s to the mid-1930s. He carefully selects examples to illustrate the “informationalization” of three aspects of identity:

first, documentary identity, illustrated in the development of birth certificates (1913); second, psychological identity, linked to the new data techniques for categorizing personality traits and measuring intelligence (1917); and third, racial identity, connected to new data techniques for real estate appraisal, such as redlining (1923).  

The informational subject thus was formed within a disparate array of administrative and technical practices of data collection, formatting, storage, and application. These practices, which highlight how governing takes place through numbers (Research Hub entry 30 May 2022), illustrate important links between genealogy and governmentality (Walters 2012). 

Koopman emphasizes the importance of providing “information” with a history. He explains that “accepting information as ahistorical facilitates our tendency to take information technologies as closed, locked and unchangeable”. Through his careful delineation of the development of birth certificates, personality tests and racial profiles he considers what could have been done differently: “we can find in that history a set of moments when data was not closed, but rather glaringly open to contestation and recomposition” (Koopman 2019: ix). 

Koopman proceeds to provide a history of “personality”, a term that is often taken-for-granted as revealing the “truth” about human development. He shows that “personality” was a new concept in the period he studies and how it came to be defined as a finite collection of measurable traits, which could be processed as data through algorithms. As McWhorter (2020) describes, according to Koopman, “personalities” are artifacts of information technology as much as they are the truths of ourselves: “they are both, simultaneously”, a point Koopman drives home forcefully.  

In terms of consequences, Koopman argues that our data have not defined us all in the same ways. “In those differences”, he says, “lies a whole terrain of power and politics” (Koopman 2019: 9). As an illustration of the costs associated with being “un-datified”, Koopman mentions the precarious position of the paperless, the undocumented, the sans papiers (Jørgensen 2012; Bacchi and Goodwin 2016: 104) – in his words, “the exception that proves the rule” (Koopman 2019: 9). 

In relation to openings for change, genealogical accounts such as Koopman’s show that the conditions which limit the present are contingently formed by extraordinarily complex historical processes (Koopman 2010: 119). The point of identifying this complexity is to eliminate overly simple causal explanations and to alert us to the multitude of factors impacting our lives – how we have got “here” from “there”. At the same time identifying the plural conditions shaping the present creates the possibility of thinking about what intervention in those processes could look like and what needs to be re-thought should change be desired. 

In the Symposium on his book (https://syndicate.network/symposia/philosophy/how-we-became-our-data/) Koopman raises the difficult question of how to resist “infopower”. He endorses Jennifer Forestal’s concern, expressed in the Symposium, that our complicity in the operations of infopolitical injustices poses the greatest challenge when it comes to the politics of data. Koopman expresses the hope that refocussing data politics around techniques of formatting would make those politics more tractable. This critique, in his view, should be largely aimed at experts and elites working in technocratic spaces, those who “build the information systems that form the basements beneath our lives” (Koopman 2019: 194). 

In terms of our research practices, Tamboukou and Ball (2003) offer some general comments on how the writing up of or writing about/around data may be deeply influenced by a genealogical sensibility. They emphasize that researchers have to become more critical about what counts as data and what does not. Specifically, they need to become more skeptical about how they locate the field of investigation and about how they choose key informants. 

To repeat a point made in the preceding entry, at some level, genealogical criticism is always self-criticism. And so, we can see how a genealogical sensibility, invoked in Question 3 of WPR, prompts a commitment to self-problematization (Step 7; Bacchi and Goodwin 2016: 20). It follows that, instead of approaching WPR as a collection of separate and separable questions, it becomes important to reflect on the ways in which its several forms of analysis constitute a way of thinking about how governing takes place. Exploring the purpose and intent of genealogy provides a useful starting point for such reflection.

REFERENCES

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan. 

Jørgensen, M.B. (2012). Legitimizing policies: How policy approaches to irregu- lar migrants are formulated and legitimized in Scandinavia. Nordic Journal of Applied Ethics, 6 (2), 46–53.

Koopman, C. 2010. Historical Critique or Transcendental Critique in Foucault: Two Kantian Lineages. Foucault Studies, 8: 100-121.  

Koopman, C. 2019. How We Became Our Data: A Genealogy of the Informational Person. Chicago: University of Chicago Press. 

McWhorter, L. 2020. Colin KoopmanHow We Became Our Data: A Genealogy of the Informational PersonUniversity of Chicago Press, 2019, 269pp., $30.00 (pbk), ISBN 9780226626581.Philosophical Reviews.

Tamboukou, M. and Ball, S. J. 2003. Introduction: Genealogy and Ethnography: Fruitful Encounters or Dangerous Liaisons? In M. Tamboukou and S. J. Ball (Eds) Dangerous Encounters: Genealogy and Ethnography. NY: Peter Lang. pp 1-36.Walters, W. 2012. Governmentality: Critical Encounters. NY: Routledge

What is the place of genealogy in WPR?

In a 2016 chapter introducing the WPR approach I explain that Question 2 in the approach undertakes a form of analysis associated with Foucauldian archaeology, while Foucauldian-style genealogy appears as Question 3 (Bacchi and Goodwin 2016: 13-26). Question 2 undertakes the task of critically analysing the “unexamined ways of thinking” (Foucault 1994: 456) that underlie problem representations. Question 3 asks how a specific representation of the “problem” has come about. 

In that chapter I also draw attention to the challenges involved in adopting genealogy as an analytic intervention. Foucault (1977: 139) describes genealogy as “gray, meticulous and patiently documentary”: “it must record the singularity of events outside any monotonous finality”. Hence, it is perhaps hardly surprising that many researchers who apply WPR tend to bypass Question 3 and to concentrate on the other questions. While, on occasion, I have said that researchers can draw selectively upon the forms of questioning and analysis in WPR, I find it increasingly important to stress the interconnected character of the WPR questions. For example, in 2016 I stressed the need to maintain a self-problematizing ethic in WPR applications, indicated in Step 7 of the approach (Bacchi and Goodwin 2016: 20). I would now encourage researchers to also include a genealogical sensibility or awareness (see Tamboukou and Ball 2003: 18-19). 

In this entry I elaborate what I mean by a “genealogical sensibility”. I also want to consider if an “abbreviated genealogy”, which I attempted in my study of “alcohol problems”, is indeed feasible (Bacchi 2015: 139-141). The subsequent entry draws upon Colin Koopman’s (2019) genealogy of the “informational person” to illustrate the usefulness of bringing a genealogical awareness to critical reflections on “data” (see Research Hub entries 29 April 2022, 30 May 2022, 29 June 2022).

As Tamboukou (1999: 201) describes, Foucault used the term “genealogy” to describe his work, but he insisted on not following any certain methodology. Hence, there is no “how to” guide available to direct the writing of genealogies. Rather, genealogy provokes a commitment to a set of broad principles rather than a strict set of methods (Foucault 1979: 139). These principles can be detected through examining how Foucault distinguishes what he produces as “genealogy” from what he calls “traditional history”. He draws the key distinctions in his 1971 essay entitled “Nietzsche, Genealogy, History” (Foucault 1977). 

It is important to note that, in this discussion of history, Foucault is referring to the craft of history – to historiography, how history is written – rather than to “history” as some assumed record of events. Foucault uses several different terms to clarify the kind of history writing he wishes to encourage. He talks, for example, about writing a “history of the present” and the need to produce “effective histories”. 

The phrase – “history of the present” – requires clarification. Foucault does not mean to imply that we should understand the present in terms of the past, a rather common view of history captured in aphorisms such as “history repeats itself”. Rather, Foucault says that one needs to start from a problem in the present. By problem, he means some concern animating the researcher’s analysis. The goal then becomes understanding the heterogeneous factors that contribute to the emergence of this particular way of organising and governing society, “attending to the plural and hybrid constitution of all things” (Walters 2006: 167). Where a traditional historian might be concerned with “what happened and why?”, a genealogist asks: “how did X get here?” or “how did Y become possible?” (Vucetic 2011: 1303).

Foucault explains his intentions in writing his genealogy of the modern penal system, Discipline and Punish (1979), thus: 

I didn’t aim to do a work of criticism, at least not directly, if what is meant by criticism in this case is denunciation of the negative aspects of the current penal system. … I attempted to define another problem. I wanted to uncover the system of thought, the form of rationality that, since the end of the eighteenth century, has supported the notion that the prison is really the best means of punishing offences in a society. … In bringing out the system of rationality underlying punitive practices, I wanted to indicate what the postulates of thought were that need to be re-examined if one intended to transform the penal system. … It’s the same thing that I tried to do with respect to the history of psychiatric institutions [in History of Madness 2006]. (Foucault 2020) 

I have highlighted the words “if one intended to transform the penal system” because I think they help us understand what Foucault meant by starting one’s analysis from a problem in the present – he means starting from a development or issue that in your view needs questioning or challenging. The perspective of the analyst is thus decisive in selecting a topic for investigation, as is the case in choosing particular policies for critical analysis in WPR (see Bacchi 2009: 20). 

On this point it is useful to recall that, in 1971, Foucault co-founded the Information Group on Prisons, a group dedicated to heightening public intolerance towards the prison system by facilitating the voices of prisoners themselves (Hoffman 2012). According to Tamboukou (1999: 213), this clear involvement of researchers in picking a starting point for critical scrutiny is not a limitation but a strength of the analysis: it “should be admitted and used by the analyst in an attempt to deconstruct possible arbitrary personal feelings and stances with regard to his/her project”.

In a genealogical study of the selected issue, the task is to understand how we have got here from there, “how this problem turned out to be the way we perceive it today” (Tamboukou 1999: 213). Importantly, the road from “there” to “here” is uneven. There is no “path dependence” in Foucault’s genealogies (Mahoney 2000). Rather, there are side-tracks, roadblocks, detours. 

By tracing out the historical conditions of possibility of our present ways of doing, being, and thinking, genealogy “couples the contingency of historical formations with their specific emergence, thereby enabling their possible transformation” (Shea 2014: 264). As Saar describes, such a stance necessarily entails “a structural reflexivity”, since it involves telling the subject “the story of its own becoming”. Genealogical criticism is always therefore self-criticism, acknowledged in Step 7 of WPR which calls upon researchers to adopt a self-problematizing ethic. Such self-interrogation assists in identifying “complicity and Implicatedness with your ‘own’ culture and its power” (Saar 2002: 236).

“Effective histories” are those that draw attention to the side-roads and detours in past and present forms of governance. They are histories that unsettle commonly accepted views and assumptions. Genealogy, following Foucault, is a work of rediscovering the “connections, encounters, supports, blockages, plays of forces, strategies, and so on” that, at a given moment, establish what subsequently counts as being self-evident, universal and necessary. Such a form of history shows that things “weren’t as necessary as all that” (Foucault 1991: 76). Such histories are effective because they demonstrate “how and why some subjects and social items were brought about and not others, what became forgotten and with what consequences for the present” (Vucetic 2011: 1302). 

Foucault settles on the notion of “historical sense” in Nietzsche as a “privileged instrument of genealogy” that operates without “the certainty of absolutes” (Tamboukou 1999: 210; Foucault in Rabinow 1986: 87). This “historical sense” evokes a kind of sensibility, or awareness, committed to “disturbing the legends of the past” and to opening up “paths for its subjects to set out for new, improbable identities” (Tamboukou 1999: 210). It achieves this effect through “the retrieval of forgotten struggles and subjugated knowledges” (Walters 2012), those minor knowledges that challenge the scientific consensus and that survive at the margins (Foucault 1980b: 83). 

It is important to remember that the conception of the subject is a primary focus of Foucault’s work, discussed in earlier Research Hub entries (30 Sept 2019; 31 Oct. 2019). There, I drew attention to the way in which Foucault emphasized that the subject has a history. If the subject is recognized as having a history, it becomes possible to see that what we understand by “being human” has “shifted radically over the ages” (Davies 1997: 22). It follows that what it means to be human is contingent and changeable, not fixed and/or transcendent (see Saar 2002: 232). As Walters (2012: 115) describes,

“Whatever its style, emphasis or source, genealogy uses historical knowledge to reveal that who and what we are is not fixed or eternal, not a matter of destiny or grand design, but a series of contingent becomings. Dis-inevitable-izing our selves: a ugly term but perhaps it captures the kind of politics genealogy shows up.”

Following from this insight a genealogical sensibility extends the commitment to historicization to a wide range of objects and subjects, indicating that they could be otherwise. 

Problematizations offer a way to access the “postulates for thought” (see above Foucault 2020) that need to be re-examined as part of this historicizing or genealogical project. For example, in his History of Sexuality, Foucault (1980a) asks how different eras have problematized sexuality and thus made sexuality a particular kind of object for thought in different sites. It follows that it is important to recognize the interconnections among Foucault’s analytic strategies. Specifically, archaeology, genealogy and problematization form a trio of interventions that prompt critical reflection on governing practices. 

“The archaeological dimension of the analysis made it possible to examine the forms of problematization themselves, its genealogical dimension enabled me to analyze the formation out of the practices and their modifications” (Foucault 1986: 17-18). 

In WPR, Question 2 targets the “forms of problematization themselves”, while Question 3 alerts us to the place of these problematizations in the production of “truth”. Question 3, therefore, forms an integral part of the analysis. 

If, as stated at the outset, there is no genealogical methodology, how is one to proceed? Tamboukou (1999: 208) describes Foucault as an “archive-addict” and I dare to suggest that few of us aspire to such a vocation. But Tamboukou also holds open a more promising development. She makes the case that the “polymorphous and diverse map of documents and sources” consulted by Foucault provides “future genealogists” with an important legacy: 

“that of going on ‘inventing’ new sources and areas of research not yet thought of by the so-called humanist sciences, so as continually to rethink and call into question the given truths of our world.” 

It is in this spirit that I continue to explore the possibility of “widening the ambit” of sources available to WPR analysis (see Research Hub entries 30 April 2021, 31 May 2021, 30 June 2021). I also believe that it is possible to consider producing abbreviated genealogies as research tools so long as a genealogical sensibility is maintained. In each case, the analysis needs to focus on disrupting taken-for-granted assumptions, to consider the types of knowledge that have been disqualified, and to reflect on the heterogeneous factors leading to a situation that, in the view of the researcher, demands rethinking. In the next entry I use Colin Koopman’s 2019 genealogy of the “informational person” to illustrate the usefulness of this form of analysis in rethinking the place of “data” in our lives. 

REFERENCES

Bacchi, C. 2015. Problematizations in Alcohol Policy: WHO’s “Alcohol Problems”. Contemporary Drug Problems, 42(2): 130-147. 

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan. pp. 13-26.

Davies, B. 1997. The Subject of Post-structuralism: A reply to Alison Jones. Gender and Education, 9(3): 271-283.

Foucault, M. (1977) [1971]. Nietzsche, genealogy, history. In D.F. Bouchard, (Ed.), Language, counter-memory, practice: Selected essays and interviews. Ithaca: Cornell University Press. pp. 139-164.

Foucault, M. 1979. Discipline and punish: The birth of the prison (trans: Sheridan, A.). New York: Vintage/Random House.

Foucault, M. 1980a. The history of sexuality, Vol. IAn introduction. New York: Vintage Books.

Foucault, M. 1980b. Two lectures. In C. Gordon (Ed.), Michel Foucault power/ knowledge: Selected interviews and other writings 1972–1977. New York: Pantheon Books. 

Foucault, M. 1986. The use of pleasure: The history of sexuality (Vol. 2). New York: Vintage.

Foucault, M. 1990. Critical theory/intellectual history. In L. Kritzman (Ed.), Michel Foucault: Politics, philosophy, culture: Interviews & other writings 1977–1984, 1st edition 1988, Sheridan, A. (trans.). London: Routledge.

Foucault, M. 1991 [1981]. Questions of method. In G. Burchell, C. Gordon, P. Miller (Eds.), The Foucault effect: Studies in governmentality. Chicago: University of Chicago Press. 

Foucault, M. 1994. So is it important to think? In J.D. Faubion, (Ed.), Power: Essential works of Foucault 1954–1984, vol. 3, Hurley, R. and others (trans.). London: Penguin.

Foucault, M. 2003. Society must be defended. In M. Bertani & A. Fontana (Eds) Lectures at the College de France, 1975-1976. NY: Picador. 

Foucault, M. 2006. History of Madness. NY: Routledge.

Foucault, M. 2020. What is called “Punishing”? In Michel Foucault, Power: The Essential Works of Michel Foucault, 1954-1984. Penguin Books. 

Hoffman, M. 2012. Foucault and the “Lesson” of the Prisoner Support Movement. New Political Science, 34(1): 21-36. 

Koopman, C. 2019. How We Became Our Data: A Genealogy of the Informational Person. Chicago: University of Chicago Press. 

Mahoney, J. 2000. Path Dependence in Historical Sociology. Theory and Society, 29(4): 507-548. 

Rabinow, P. (Ed.) 1986. The Foucault Reader. London: Peregrine.

Saar, M. 2002. Genealogy and Subjectivity. European Journal of Philosophy, 10(2): 231-245. 

Shea, G. W. 2014. Review of Colin Koopman, Genealogy as Critique: Foucault and the Problems of Modernity, 2013. Foucault Studies, 17: 264-267.

Tamboukou, M. 1999. Writing Genealogies: an exploration of Foucault’s strategies for doing research. Discourse: Studies in the Cultural Politics of Education, 20(2): 201-207. 

Tamboukou, M. and Ball, S. J. 2003. Introduction: Genealogy and Ethnography: Fruitful Encounters or Dangerous Liaisons? In M. Tamboukou and S. J. Ball (Eds) Dangerous Encounters: Genealogy and Ethnography. NY: Peter Lang. pp 1-36.

Vucetic, S. 2011. Genealogy as a research tool in International Relations. Review of International Relations, 37(3): 1295-1312. 

Walters, W. 2006. “The End of the Passing Past”: Towards a Polytemporal Policy Studies. In G. Marston and C. McDonald (Eds) Reframing Social Policy: A Governmental Approach. London: Edward Elgar. pp. 167-186. Walters, W. 2012. Governmentality: Critical Encounters. NY: Routledge

Research in a time of metadata, Big Data, data curation, data mining, data science, etc.

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”. 

REFERENCES

Bacchi, C. 2001. Dealing with “difference”: beyond “multiple subjectivities”. In P. Nursey-Bray & C. Bacchi (Eds.), Left Directions: Is There a Third Way? Perth: University of Western Australia Press, pp. 110-122.

Bacchi, C. 2009. Analysing Policy: What’s the Problem Represented to be? Frenchs Forest: Pearson Education.

Bacchi, C. 2012. Strategic interventions and ontological politics: Research as political practice. In A. Bletsas and C. Beasley (Eds) Engaging with Carol Bacchi: Strategic Interventions and Exchanges. Adelaide: University of Adelaide Press. pp. 141-156.

Bacchi, C. 2017. Policies as Gendering Practices: Re-Viewing Categorical Distinctions. Journal of Women, Politics and Policy, 38(1): 20-41.   

Bacchi, C. and Bonham, J. 2016. Poststructural Interview Analysis: Politicizing “personhood”. In C. Bacchi and S. Goodwin, Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan. pp. 113-121.

Bacchi, C. and Eveline, J. 2010. Mainstreaming Politics: Gendering practices and feminist theory. Adelaide: University of Adelaide Press. 

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan.

Beer, D. 2016. How should we do the history of Big Data? Big Data & Society, January – June, 1-10. 

Bowker, G. 2013. Data Flakes: An Afterward to “Raw Data” is an Oxymoron. In In L. Gitelman and V. Jackson (Eds) “Raw data” is an oxymoron.  Cambridge, Massachusetts: MIT Press. pp. 169-171.

Bryman, A. 2006. Integrating quantitative and qualitative research: how is it done? Qualitative Research, 6(1): 97-113.

Cambrioso, A., Bourret, P., Rabeharisoa, V., and Callon, M. 2014. Big Data and the Collective Turn in Biomedicine: How Should We Analyze Post-genomic Practices? Tecnoscienza: Italian Journal of Science & Technology Studies, 5(1): 11-42.

Deleuze, G. and Guattari, F. 1988. A Thousand Plateaus: Capitalism and schizophrenia. London: Athlone Press. 

Fordyce, R. and Jethani, S. 2021. Critical data provenance as a methodology for studying how language conceals data ethics. Continuum: Journal of Media & Cultural Studies.https://doi.org/10.1080/10304312.2021.1983259

Garcia, N. M., López, N. and Vélez, V. N. 2018. QuantCrit: rectifying quantitative methods through critical race theory. Race Ethnicity and Education, 21(2): 149-157.   

Gillborn, D., Warmington, P. & Demack, S. 2018. QuantCrit: education, policy, “Big Data” and principles for a critical race theory of statistics. Race Ethnicity and Education, 21(2): 158-179.

Koro-Ljungberg, M. and MacLure, M. 2013. Provocations, Re-Un-Visions, Death, and Other Possibilities of “Data”. Cultural Studies, Critical Methodologies, 13(4): 219-222. 

Lather, P. 2013. Methodology-21: what do we do in the afterward? International Journal of Qualitative Studies in Education, 26(6): 634-645.

Latour, B. et al. 2012. “The whole is always smaller than its parts” – a digital test of Gabriel Tardes’ monads. The British Journal of Sociology, 63(4): 590-615.

Leonelli, S. 2014. What difference does quantity make? On the epistemology of Big Data in biology. Big Data & Society1(1): 1-11. 

Leonelli, S. 2017. Data Shadows: Knowledge, Openness, and Absence. Science, Technology, & Human Values, 42(2): 191-202. 

Mol, A. 2002. The body multiple: Ontology in medical practice. Durham and London: Duke University Press.

Sheller, M. (2014). Vital methodologies: Live methods, mobile art, and research-creation. In P. Vannini (Ed.), Non-representational methodologies: Re-envisioning research (pp. 130-145). New York, NY: Routledge. 

Springgay, S. and Truman, S. E. 2018. On the Need for Methods Beyond Proceduralism: Speculative Middles, (In) Tensions, and Response-Ability in Research. Qualitative Inquiry, 24(3): 203-214.  

St. Pierre, E. A. 2006. Scientifically based research in educa- tion: Epistemology and ethics. Adult Education Quarterly56: 239-266. 

St Pierre, E. 2011. Post Qualitative Research: The Critique and the Coming After. In N. Denzin and Y. Lincoln (eds) The Sage Handbook of Qualitative Research, 4th edn. Thousand Oaks, CA: Sage. pp. 611-25.

St. Pierre, E. A. 2016. The Empirical and the New Empiricisms. Cultural Studies, Critical Methodologies, 16(2): 111-124. 

St Pierre, E. 2019. Post Qualitative Inquiry in an Ontology of Immanence. Qualitative Inquiry 25(1): 3-16.

Street, C. et al. 2021. Do numbers speak for themselves? Exploring the use of quantitative data to measure policy ‘success’ in historical Indigenous higher education in the Northern Territory, AustraliaRace Ethnicity and Education, https://doi.org/10.1080/13613324.2021.2019003 Tamboukou, M. and Ball, S. J. (Eds) 2003. Dangerous Encounters: Genealogy and Ethnography. New York: Peter Lang

“The data is in”: Governing through “data”

The quote in the title comes from a Radio National interview (Australia) conducted on the Breakfast program on 24 November 2021 (Yes, I was walking). The interviewee was asked about women’s excessive workload, at home and elsewhere, during the pandemic. She replied: “The data is in”, and it revealed that women did the bulk of home schooling, etc. 

Since being prompted to write about the topic of “data” and research by a participant in the recent “Kick-off” event for the planned international WPR symposium in August 2022 (https://www.kau.se/statsvetenskap/forskning/international-symposium-critical-policy-studies-exploring-premises-and I have paid particular attention to the ways in which the term “data” appears in our conversations and in public pronouncements, by government spokespeople and researchers (see last entry 29 April 2022). As with many of the other topics I’ve broached over the past four years in this Research Hub, enquiring into the status of “data” as research material opened a Pandora’s box.

I decided to explore the issue under two broad interconnected headings: 

  1. The place of “data” in governing (this entry)
  2. The place of “data” in research (subsequent entry)

This distinction is drawn purely for heuristic purposes. From the outset I need to emphasize that how researchers “use” “data” needs to be conceived as a part of governing, as a governing practice. Drawing upon Foucault’s notion of governmentality, governing is seen to involve a broad array of agencies and groups, including professionals, experts and researchers

 On this point, it is helpful to keep in mind Annemarie Mol’s (2002: 155, emphasis in original) argument 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”. Therefore, the production of “data”, through research, is understood to be a form of political practice that creates “realities” (see Bacchi 2012: 143). In this view, “data” operate as governmental technologies, mechanisms through which governing – understood broadly – takes place (Bacchi and Goodwin 2016: 44), as elaborated below.

With this backdrop, I advance two propositions:

first, that “data” is produced and hence it is important to reflect on the means of its production; and

second, that “data” is productive – that it produces “things” including “objects”, “subjects” and “places” (see Benozzo et al. 2013: 309). 

Readers may recognize that here I am questioning (or problematizing) “data” much in the same way I problematize “problems” in WPR – where I point out that “problems” do not simply exist out there waiting to be “solved” but that they are produced in policies as particular sorts of problem (Bacchi and Goodwin 2016: 14). I also emphasize in WPR that how “problems” are produced involves the categorization (and making “real”) of “objects”, “subjects” and “places”. Both in reference to “problems” and to “data”, therefore, the emphasis is on the production of “reality” as a performative practice (see  BACCHI KICK-OFF PRESENTATION). In poststructuralism, “a performative is that which enacts or brings about what it names” (de Goede 2006: 10)

“Data” and “problems” are linked in a second way. “Data” are generally put forward to solve “problems” – assumed to exist as clear and unquestionable states or conditions. As Valentine (2019: 365) describes, “Simply put, algorithms are mathematical processes for solving defined problems”. Hence, “data” form part of what I call the problem-solving knowledge that dominates the contemporary intellectual and policy landscape (Bacchi 2020; see also Edwards et al., 2021). The common use of “data” as “evidence” in evidence-based policy approaches confirms this link (Street et al. 2021: 1). Morozov (in Schüll 2013) labels this tendency “technological solutionism” – recasting complex social phenomena such as politics, public health, education and law enforcement as “neatly defined problems with definite, computable solutions”. As a contemporary example of “technological solutionism”, consider the former Australian Prime Minister, Scott Morrison’s, “action plan” for climate change which he characterized as reliant on “technology, not taxes” (https://www.abc.net.au/news/2021-04-23/pm-defends-his-technology-not-taxes-approach-to-emissions/13315616

Generally, “data” have a taken-for-granted status as unquestioned information (Gitelman and Jackson 2013: 10).  In addition, “data” are treated as the basis of knowledge, understood as “truth”. The word “data” sits alongside allied terms such as “facts” and “evidence”. If you wish to “prove” something, you need to be able to provide back-up “data”. 

This conception of “data” lines up with a correspondence view of knowledge. “Data” are “found” in the “real world” and hence provide “knowledge” of that world – assumed to exist as fixed and inert, waiting to be known. In other words, “data” is not an innocent term. It brings with it an ontology and an epistemology, a way of thinking about “reality” and how to “interact” with “it”.

Foucauldian-influenced research offers, as an alternative to realist knowledge, a focus on how knowledges (note the plural form; see below) are produced. As argued in an earlier entry (29 Nov. 2021), in Foucauldian-influenced perspectives, “truth” is always situated. There is no universal basis for “truth”. Rather, “truth” and “knowledge” are produced in “‘local centres’ of power-knowledge” (Foucault 1990: 98). The analytic task, therefore, involves seeking out and examining the multitudes of practices – the “processes, procedures and apparatuses” (Tamboukou 1999: 202) – involved in the production of “truth” (or “truths”), rather than (simply) to uncover what is concealed. The goal becomes showing how political practice takes part in the “conditions of emergence, insertion and functioning” of “regimes of truth” (Foucault 1972: 163).

What does it mean to track how “data” are produced? What sorts of processes do we need to identify?  There are several levels at which this topic could be pursued. At one level it is possible to draw attention to where data come from (Fordyce and Jethani 2021: 3) – who asks the questions that produce data? What authority do they have?  At this level, there are expressed concerns to regulate or oversee the practices involved in the production and control of “data” in order to monitor and regulate surveillance (see Raley 2013). Padden and Öjehag-Pettersson (2021) usefully subject one such declared attempt at regulation, the EU GDPR (General Data Protection Regulation), to critical analysis using WPR. 

At a second level, “data” are produced as particular kinds of objects because they are organized in specific ways. “Data” do not speak for themselves. Through categorization and visualization, they are made to speak. Data are not just found; they are imagined (Gitelman and Jackson 2013: 3) and generated (Manovick 2020). 

Furthermore, the forms of categorization and organization that produce “data” have productive (constitutive) effects – creating “subjects”, “objects” and “places” of specific kinds. Through these effects, “data” play an active governing role, forming what governmentality scholars refer to as “technologies of rule” (Bacchi and Goodwin 2016: 44). In this view governmental instruments such as censuses, league tables and performance “data” are involved in “the conduct of conduct” (Gordon 1991: 2). 

For example, Rowse (2009) explores how the current Australian census arranges the “population” into categories (such as Indigenous and non-Indigenous) with repercussions for the sorts of political claims that can be made. In this account, numbers operate, not as a neutral, statistical register of the “real”, but as involved in shaping political possibilities. They are “integral to the problematisations that shape what is to be governed, to the programmes that seek to give effect to government, and to the unrelenting evaluation of the performance of government that characterises modern political culture” (Rose 1991: 175; see also Miller 2001).

Illustrating this point Harrington (2021) shows how the UN Secretary-General’s requirement to report on SEA (sexual exploitation and abuse) “data” shapes solutions: “The reports reflect managerial audit systems that produce a performance culture in which the goal becomes performing well for the audit”. Audit systems demand quantifiable progress in “solving” assumed “policy problems”: “Audits of not-readily quantifiable performances must contrive ways to quantify them, often with far-reaching consequences”. 

Instead of reporting on the “real”, “data” – numbers and statistics – are involved in constituting “the real”. They are involved for example in the production of an “urban/rural” dichotomy, and in the distinction between “developed” and “developing” “places” (Bacchi and Goodwin 2016: 100-104). Walters (in Tietäväinen et al. 2008: 67) describes how “Europe” has become a “domain of statistics, calculation and projections”, firming up its existence as an entity.

Fernandez (2012: 57) reminds us that the focus on statistics transforms a political issue into a technical one, described above as “technological solutionism”. She illustrates how the BLP [Below the Poverty Line] census of “poor families” used by the Indian government constitutes poverty as “lack of income”, silencing the inequitable distribution of power and access to resources. The accompanying proposal to reduce poverty by organizing the “rural poor”, specifically women, into self-help groups, creates “subjects” held to be responsible for their own welfare. 

The Programme for International Students Assessment (PISA) provides another example of how governing by numbers takes place. The Programme tabulates and compares national literacy proficiency indicators in the areas of reading, numeracy and science, indicators which translate into assessments of students, their “skills”, and labour-market performance. The Foreword to the summary of PISA 2012 results on “Creative Problem Solving” makes this connection explicit: 

“highly skilled adults are twice as likely to be employed and almost three times more likely to earn an above-median salary than poorly skilled adults.” (OECD, 2014: 3; emphasis added) 

What “skills” entail and how they are conceptualized are taken for granted, as is the computation of “success” (an above-median salary). This decision to classify students by their standardized achievement and aptitude tests “valorizes some kinds of knowledge skills and renders other kinds invisible” (Bowker and Star 2000: 5-6). As Stephen Ball (2020) describes, “the relationships of truth and power are articulated and operationalized more and more in terms of forms of performance, effects or outputs and outcomes, all expressed in the reductive form of numbers”, resulting in the “numericisation of politics” (Legg 2005: 143). 

Numbers and statistics also play a key role in the production of risk categories and statistical projections. As Dean (2010: 206) reminds us: 

“There is no such thing as risk in reality …Risk is a way, or rather a set of different ways, of ordering reality, of rendering it into a calculable form. It is a way of representing events in a certain form so they might be made governable in particular ways, with particular techniques and particular goals”. 

For example, Lancaster et al. (2020) describe how “Numbering and calculation practices have a key role in the creation, monitoring and regulation of risk populations and risk factors in public health (Castel 1991; Petersen 1997), linked to the rise of modern epidemiology and ‘surveillance medicine’ (Armstrong 1995)”. They highlight the importance of reflecting on the “forms of action” (Rowse 2009: 45; emphasis in Rowse) made possible by these calculative practices (and one might add the “forms of action” rendered irrelevant). The prominent role played by modelling in the COVID-19 pandemic provides a recent example of an intervention where such reflection is called for (Rhodes and Lancaster 2021; Rhodes et al. 2020). We need to ask: what “forms of action” have been rendered acceptable through the models produced to govern COVID-19? What possible “forms of action” go undiscussed? 

Edwards et al. (2021) show how risk categories play an increasing role in welfare governing, which they describe as an exercise in the “datafication” of citizens. People are turned into data: “identifying and categorising them to predict future behaviour, allocate resources, and determine eligibility for services and interventions” (Edwards et al. 2021: 2). As the authors explain, “The data are regularly updated, integrated and subject to application of algorithmic tools and predictive risk modelling”. One noteworthy outcome is the way in which particular families are targeted as “problematic”. Peterson and Lupton (1996) describe how this form of targeting is individualizing, “given the tendency to identify ‘risks’ in people’s lives while leaving them responsible to reshape their lives to meet those risks”. 

Statistical risk assessments also function prominently in the criminal justice system. For example, Padden and Öjehag-Pettersson (2021) highlight the role of algorithmic profiling 

“to detect welfare and tax fraud, assess the likely truthfulness of police complaints, assist in sentencing decisions, and make predictions or trigger ‘risk alerts’ in child welfare, policing, eldercare and psychiatric services (Dencik et al. 2018; AlgorithmWatch 2019)”. 

“Predictive policing” algorithms are now being used in law enforcement to determine areas where crime is likely to occur. “Re-offending algorithms” are also being used as a part of sentencing. Elliott Smith (2019: 8-10) reminds us that such algorithms, which necessarily require simplification in order to work, are reductive and reflect prevailing prejudices, “making bigots of us all” (see Street et al. 2021: 159). For example, Edwards et al. (2021: 15) show how the predictive risk modelling used in child protection obscures the in-built equation of socio-economic disadvantage with risk, building in discrimination against the poor. 

Importantly, Gitelman and Jackson (2013: 8) point out that “data aren’t only or always numerical”. However, while they do not always exist in number, data are treated as particulate – in the form of separate particles. For example, “qualitative data” and “interview data” involve attention to specific bits of “information”, raising the suggestion that references to “data” in such contexts may well be metaphorical (pursued in next entry) (Gitelman and Jackson 2013: 10). 

Because data are particulate, data practices are necessarily aggregative – “They [data] are collected in assortments of individual, homologous data entries and are accumulated into larger or smaller data sets” (Gitelman and Jackson 2013: 8; emphasis in original). It is these practices of organization, classification and deployment that need to be tracked. The goal, according to Bowker and Star (2000: 4), is to identify the origins and consequences of a range of social categories and practices. 

You may have noticed that throughout this entry I have used “data” as a plural noun – referring for example to “data are” rather than to “data is” (unless I am quoting someone who uses the singular form as in the title).  I apologize to those of you who find grammar uninteresting and hope you may come to see that there is a great deal at stake in whether “data” is treated as “it” or as “they”. I should caution that adopting “data” as a plural is not always easy to implement, given the common singular usage. I had to go back to correct this entry in several places where I had slipped inadvertently into talking about data as “it”.

There appear to be two camps of thought on the topic. The Oxford English Dictionary accepts that the concept of “data” is generally not treated as a plural but as a mass noun (like “information”), an “uncountable noun” that takes a singular verb (i.e., data is) (https://www.lexico.com/definition/data). By contrast, the Publication Guidelines for the American Medical Association state that the singular noun is “datum”, and the plural is “data”, the latter necessitating a plural verb (i.e. data are) (https://amastyleinsider.com/tag/clear-and-concise/).  

I prefer and promote the second plural option on the grounds that treating “data” as a singular item, a “mass noun”, produces “it” as a “thing”, with some sort of fixed parameters. In the process the practices involved in “its” production and organization go unobserved and unanalysed. Using “data” in the plural provides a simple way to ensure that how “data” are produced becomes a necessary feature of one’s analysis. Poststructuralists use a similar stratagem in references to knowledgesrather than to knowledge, which is commonly characterized as a “mass noun”.

Clearly, “mass nouns” are not innocent. They assume a specific epistemology and have political effects. They reflect a trend among users of the English language to “focus more on categories and classifications which define a thing and fix its nature for all time” with less concern for “processes, movement and change” (Hoagland 1988: 224). A poststructural approach to concepts and categories aims to return attention to those processes.   

Other attempts to highlight the fluid and constructed character of “data” include references to “datafication” (see above regarding “datafication of citizens”) and “datafying”. However, these terms have already been co-opted by data analysis companies.  “Datafication” is now commonly adopted to refer to “the collective tools, technologies and processes used to transform an organization to a data-driven enterprise”(https://www.techopedia.com/definition/30203/datafication). In this understanding an organization that implements datafication is said to be “datafied” (https://datafying.co).

Elsewhere I have written about the possible use of gerunds (turning a noun into a verb form by adding “ing”) as a poststructural conceptual strategy to direct attention to the ongoing formation of “things” deemed to be fixed in place and/or time. The concepts of “gender-ing” and “border-ing” are put forward as a means of directing attention to the practices involved in the becoming of “things”, rather than simply accepting them as unchanging states of being (Bacchi 2017; Bacchi and Goodwin 2016: 100). For data, then, we might try referring to “data-ing” – though I suspect that such an awkward term is unlikely to become popular.  

Clearly, then, researchers face many dilemmas in relation to “data”, including the rather basic task of how to refer to data without inadvertently producing “it” as a politically neutral singularity. More broadly, returning to the question/comment in the “Kick-off” event, there is an ongoing presumption in mainstream social science studies that research arguments will be “backed up” by data. At the same time and in dramatic contrast, recent debates around the need for “post-qualitative” research put into question the use of all forms of “humanist” research (St Pierre 2013: 226), including research that relies on data. How are researchers to negotiate this minefield? I reflect on this topic in the next entry.  

REFERENCES 

AlgorithmWatch. 2019. “Automating Society – Taking Stock of Automated Decision-Making in the EU.” https://algorithmwatch.org/wp-content/uploads/2019/02/Automating_Society_ Report_2019.pdf 

Armstrong, D. 1995. The rise of surveillance medicine. Sociology of Health & Illness, 17(3): 393-404.

Bacchi, C. 2012. Strategic interventions and ontological politics: Research as political practice”. In A. Bletsas and C. Beasley (Eds) Engaging with Carol Bacchi: Strategic Interventions and Exchanges. Adelaide: University of Adelaide Press. Available as a free download from the publisher’s website: https://www.adelaide.edu.au/press/

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. New York: Palgrave Macmillan.

Bacchi, C. 2017. Policies as Gendering Practices: Re-Viewing Categorical Distinctions. Journal of Women, Politics and Policy, 38(1): 20-41.  

Bacchi, C. 2020. Problem-Solving as a Governing Knowledge: “Skills”-Testing in PISA and PIAAC. Open Journal of Political Science, 10, 82-105.

Ball, S. J. 2020. Foreword. In E. Grimaldi, An Archaeology of Educational Evaluation: Epistemological Spaces and Political Paradoxes. London and New York: Routledge.

Benozzo, A., Bell, H. and Koro-Ljungberg, M. 2013. Moving Between Nuisance, Secrets, and Splinters as Data. Cultural Studies, Critical Methodologies, 13(4): 309-315.

Bowker, G. C. and Star, S. L. 2000. Sorting Things Out: Classification and its Consequences. MIT Press.  

Castel, R. 1991. From dangerousness to risk. In G. Burchell, C. Gordon and P. Miller (Eds) The Foucault Effect: Studies in Governmentality.Hemel Hempstead: Harvester Wheatsheaf 

Dean, M. 2010. Governmentality: Power and Rule in Modern Society. Second ed. New York: Sage. 

de Goede, M. 2006. International political economy and the promises of poststructuralism. In M. de Goede (Ed.), International political economy and poststructural politics. Houndmills: Palgrave Macmillan.

Dencik, L., A. Hintz, J. Redden, and H. Warne., 2018. “Data Scores as Governance: Investigating Uses of Citizen Scoring in Public Services. Project Report.” Data Justice Lab. Cardiff University, UK. December. https://datajustice.files.wordpress.com/2018/12/data-scores-as-governance- project-report2.pdf

Edwards, R., Gillies, V. and Gorin, S. 2021. Problem-solving for problem-solving: Data analytics to identify families for service intervention. Critical Social Policy, DttOpsI:/1/d0o.i1.o1rg7/71/0.12167170/0128631201813021012092042jo9u4rnals.sagepub.com/home/csp. Rosalind Edwards, SSPC, University of Southampton, SO17 1BJ, England, UK. 

Elliot Smith, R. 2019. Rage Inside the Machine: The Prejudice of Algorithms, AND How to Step the Internet Making Bigots of Us All.London: Bloomsbury Business. 

Fernandez, B. (2012). Transformative policy for poor women: A new feminist framework. Surrey: Ashgate. 

Fordyce, R. and Jethani, S. 2021. Critical data provenance as a methodology for studying how language conceals data ethics. Continuum: Journal of Media & Cultural Studies.https://doi.org/10.1080/10304312.2021.1983259 

Foucault, M. 1972. The archaeology of knowledge, and the discourse on language

(trans: Sheridan Smith, A.M.). New York: Pantheon Books.

Foucault, M. 1990. The history of sexuality. Volume I: An introduction

(trans: Hurley, R.). New York: Vintage Books.

Gitelman, L. and Jackson, V. 2013. Introduction. In L. Gitelman and V. Jackson (Eds) “Raw data” is an oxymoron.  Cambridge, Massachusetts: MIT Press. pp. 1-14.

Gordon, C. 1991. Governmental Rationality: An Introduction. In Burchell, G., Gordon, C. and Miller, P. (eds) The Foucault Effect: Studies in Governmentality. Chicago: University of Chicago Press. pp. 1-51.

Harrington, C. 2021. United Nations Policy on Sexual Exploitation and Abuse: Problematizations and Performances. Critical Social Policy, 1-21. 

Hoagland, S.L. 1988. Lesbian ethics: Toward new value. Palo Alto, CA: Institute 

of Lesbian Studies.

Lancaster, K., Rhodes, T. and Rance, J. 2020. “Towards eliminating viral hepatitis”: Examining the productive capacity and constitutive effects of global policy on hepatitis C elimination. International Journal of Drug Policy, 80: 102419.

Legg, S. 2005. Foucault’s population geographies: Classifications, biopolitics and govern- mental spaces, Population Space and Place, 11 (3), 137–156. 

Manovich, L. 2020. Data. In P. Heike, Critical Terms in Futures Studies. Palgrave.

Miller, P. 2001. Governing by numbers: Why calculative practices matter. Social Research, 68(2): 379-396.

Mol, A. 2002. The body multiple: Ontology in medical practice. Durham and London: Duke University Press.

OECD 2014. PISA 2012 Results: Creative Problem Solving: Students’ Skills in Tackling Real-Life Problems (Volume V). Paris: PISA, OECD Publishing. 

Padden, M. and Öjehag-Pettersson, A. 2021. Protected how? Problem representations of risk in the General Data Protection Regulation (GDPR). Critical Policy Studies, DOI: 10.1080/19460171.2021.1927776  

Petersen, A. 1997. Risk, governance and the new public health. In A. Petersen & R. Bunton (Eds) Foucault, health and medicine. London: Routledge. pp. 189-206.

Petersen, A., & Lupton, D. 1996. The new public health: Health and self in the age of risk. London, England: Sage.

Rhodes, T., Lancaster, K., Lees, S. & Parker, M. 2020. Modelling the pandemic: Attuning models to their contexts. BMJ Global Health, 5:e002914, doi:10.1136/ bmjgh-2020-002914.

Rhodes, T. & Lancaster, K. 2021. Excitable models: Enumerated projections and the making of futures without disease. Sociology of Health and Illness, (Advance online publication), DOI: 10.1111/1467-9566.13263.

Rose, N. 1991. Governing by numbers: Figuring out democracy. Accounting Organizations and Society, 16(7): 673-692.

Rowse, T. (2009), The Ontological Politics of “Closing the Gaps”, Journal of Cultural Economy, 2 (1-2): 33-49.

Schüll, N. D. 2013. The folly of technological solutionism: An Interview with Evgeny Morozov. Public Books: A Magazine of Ideas, Arts and Scholarship. Available at: https://www.publicbooks.org/the-folly-of-technological-solutionism-an-interview-with-evgeny-morozov/

St Pierre, E. 2013. The appearance of data. Cultural Studies, Critical Methodologies, 13(4): 2230227.

Street, C. et al. 2021. Do numbers speak for themselves? Exploring the use of quantitative data to measure policy ‘success’ in historical Indigenous higher education in the Northern Territory, AustraliaRace Ethnicity and Education, https://doi.org/10.1080/13613324.2021.2019003

Tamboukou, M. 1999. Writing genealogies: An exploration of Foucault’s strategies for doing research. Discourse: Studies in the Cultural Politics of Education, 20 (2), 201–217.

Tietäväinen, A., Pyykkönene, M., Kaisto, J. 2008. Globalization and power – governmentalization of Europe? An interview with William Walters. Foucault Studies, 5, 63–73.   Valentine, S. 2019. Impoverished Algorithms: Misguided governments, flawed technologies, and social control. Fordham Urban Law Journal, 46(2): 364-425

Red alerts and political thinking: Preliminary thoughts on “data”

Every day (unless it is ferociously stormy, which happens seldom in Adelaide) I take a 45-minute morning walk and listen to the Australian public broadcaster, Radio National. I am a creature of habit! This morning (Dec 3, 2021) two items caught my attention.  They both mentioned “data”, and this is the topic I’ve been thinking about. I was prompted to reflect on “data” by a question/comment in the “Kick-off” event for the scheduled International WPR Symposium in Karlstad, August 2022 (https://www.kau.se/statsvetenskap/forskning/international-symposium-critical-policy-studies-exploring-premises-and 

The researcher noted that she had been challenged to explain how she used “data” given that WPR is an interpretive approach. This question of the relationship between “data” and research methods is highly topical and will be pursued in subsequent entries. 

Returning to my morning walk and the radio program, in one item, a Melbourne academic, Dr Lauren Rosewarne, commented on the Federal Government’s proposed “anti-trolling” legislation. Under the proposed legislation, the laws would require social media companies to collect personal details of current and new users, and allow courts to access the identity of users to launch defamation cases. 

Dr Rosewarne raised several concerns. The proposed legislation, she noted, was complaint-based, and hence relied upon individuals having the resources to pursue complaints. She also asked if the listeners wanted social media companies to hold “data” on them. At one point she made this additional point: “The solution doesn’t seem to match the policy problem from my perspective”. My ears pricked up at the mention of “problems” (trolling) and “solutions”. Those familiar with WPR will be able to see its thinking at work in the analysis produced. 

Dr Rosewarne pointed out that the “postulated solution” (the policy) produced the “problem” as defamation. She elaborated on the inadequacy of this approach. Defamation of character, she explained, which politicians assume characterizes abuse online, does not cover the forms of harassment trolling entails. Things are missing from this analysis (WPR, Question 4), with severe limitations for the usefulness of the intervention (WPR, Question 5; see Chart below.).

I would like to suggest that the terms “problems” and “solutions” serve as “red alerts”, stopping us in our tracks and impelling us to apply WPR thinking.  The hope is that such a strategy produces a useful form of political thinking. 

The second item that caught my attention, reported in the Radio National News on 3 Dec 2021, was on emissions measurement (I was still walking!). It was based on a Dutch study which claimed that the Australian government was underreporting levels of emissions (https://www.abc.net.au/news/2021-12-03/new-data-suggests-australia-could-be-underreporting-methane/13660496

The headline, indicated in the link, read: “New data suggests Australia could be underreporting …”. One point jumped out in the summary of the Dutch report – it seems the Australian government received its emissions data from the oil companies. So “data” proved useful in questioning “data”. The point resonated with some of the reading I have been doing around “data” – that they [note the plural usage; explained in next Research Hub entry] can be useful to some researchers and that, at the same time, “data” are not simply inert “facts” but that they are produced in social processes. These are themes I intend to pursue in subsequent entries.

The point of this very brief interlude is to suggest that I was sensitized to the political relevance of “data” by my recent reading and by the appearance of the issue as a topic of concern at the “Kick-off” event. For me, “data” is now a “red alert” term. I now notice the term “data”, whereas previously it had operated as a taken-for-granted concept that escaped attention. I wonder if readers might like to share with us some other “red alert” terms. I could post them in a subsequent entry. I would also love examples where WPR became useful for you in your daily encounters with the news or some political announcement, where it prompted what I would like to call political thinking

What’s the Problem Represented to be? (WPR) approach to policy analysis 

Question 1: What’s the problem (e.g., of “gender inequality”, “drug use/abuse”, “economic development”, “global warming”, “childhood obesity”, “irregular migration”, etc.) represented to be in a specific policy or policies? 

Question 2: What deep-seated presuppositions or assumptions underlie this representation of the “problem” (problem representation)? 

Question 3: How has this representation of the “problem” come about? 

Question 4: What is left unproblematic in this problem representation? Where are the silences? Can the “problem” be conceptualized differently? 

Question 5: What effects (discursive, subjectification, lived) are produced by this representation of the “problem”?
Question 6: How and where has this representation of the “problem” been produced, disseminated and defended? How has it been and/or how can it be disrupted and replaced? 

Step 7: Apply this list of questions to your own problem representations.

C. Bacchi and S. Goodwin (2016) Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan, p. 20.