In the last Research Hub entry (29 Sept 2023) I experimented with ChatGPT, asking targeted WPR questions. I concluded that the chatbot did not perform well in terms of understanding the theory underpinning WPR and that it could only summarize existing material and that to which it was explicitly directed. None of this should come as a surprise. As King (2023: 105) relates, as long ago as the 1830s, Ada Lovelace explained that Charles Babbage’s Analytic Engine – widely regarded as the world’s first computer – “has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform”. 

While this limitation may be widely recognized, in another development, the benefits and learning outcomes associated with using computers are increasingly lauded. The term “Computational Thinking” (CT) is becoming prevalent in educational curricula and in associated testing regimes, as a shorthand for those benefits. PISA’s 2021 ICT Framework (OECD 2019: 3.2.2 para 63) recognizes “computational thinking” as an ICT competency within the mathematics curriculum:

         “In this context, computational thinking refers to formulating problems and designing their solutions in a form that can be executed by or with a computer (Cuny, Snyder and Wing 2010).” (OECD 2019: 6.1.1 para 168; emphasis added). 

The literature on CT (Computational Thinking) is voluminous and repeats some common themes: first, that CT refers to the thought processes stimulated by working with computers; and second, that those thought processes represent a valuable way of thinking that students need to absorb due to our increasingly digitalized world.

In this entry I raise questions about CT as a recommended thinking style, emphasizing how it relies upon and operates through a problem-solving analytic that presumes “problems” as pre-existing entities.  The argument proceeds through several stages. First, I present common claims concerning the educational benefits of CT. Next, I raise some of the useful criticisms that have been developed. Finally, I make the case that CT operates within a problem-solving framework that sets the terms of reference for its operation, a framework that imposes severe limitations on CT as a way of thinking.

 What CT claims to offer

Lodi and Martini (2021) offer a useful introduction to the history of “computational thinking” (see also Tedre and Denning 2016). They trace the emergence of the field of computing as an autonomous discipline to the end of the 1950s and the early 1960s. The contributions of two researchers, Seymour Papert and Jeannette Wing, to current thinking on the topic are highlighted.  

The expression “Computational Thinking” (CT) was used for the first time by Papert in 1980 (Lodi and Martini 2021: 884). For Papert, CT is the result of his constructivistapproach to education, “where social and affective dimensions are as important as the technical content” (Lodi and Martini 2021: 884). Wing (2006) revived the term CT in a much-quoted 2006 paper. For Wing CT is the result of teaching Computer Science. She (2010) makes the case that learning to think like a computer scientist would be a benefit for everyone, in whatever profession involved. 

Following Wing’s intervention, initiatives to bring computer science into all levels of K-12 education were introduced in many countries. Wing (2010: 4-5) produces a long list of courses and programs in CT in American and UK professional organizations, government, academia, and industry. This growth, it should be noted, is not replicated in the Global South (Belmar 2022). 

Proposals to teach CT emphasize that training in computer science produces a cluster of “skills” or “mental strategies” “useful to solve problems” (Lodi and Martini 2021: 896). The most commonly listed “skills” include: abstraction, decomposition, pattern generalisation, debugging and algorithm design. There is an occasional mention of “critical thinking”, though what is meant by the term is not elaborated (Shute et al. 2017). 

In the growth and proliferation of interest in CT a major issue concerns whether or not the “mental strategies” associated with computer use automatically transfer to other disciplines. According to Lodi and Martini (2021: 884; emphasis in original), both Papert’s and Wing’s CT “come (or seem to come) with the idea that the competencies acquired as CT will easily (or even automatically) transfer to other disciplines”. And, they argue, this “largely unverified” assertion about the transferability of intellectual skills, in both Papert and Wing, helps to explain the wide appeal of CT (Lodi and Martini 2021: 884).  

It is important to recognize the distinctiveness of Papert’s (1980) understanding of CT. As Lodi and Martini (2021: 900) explain, transfer, in Papert, “is something that does not happen automatically, and needs an active and deliberate effort”. Links can be drawn between this 1980s iteration of CT and a “student-friendly” version of problem-solving, associated with John Dewey and with cognitive constructivism (Bacchi 2020). For Papert, “only a social and affective involvement of the student in constructing a (computational) artifact will make programming an interdisciplinary tool for learning (also) other disciplines” (Lodi and Martini 202: 883). Wing (2010: 5), by contrast, makes a straightforward claim that “The educational benefits of being able to think computationally transfer to any domain by enhancing and reinforcing intellectual skills”.

Among those intellectual skills that will (it is argued) be transferred, Wing emphasizes the importance of abstraction. According to Wing (2010: 1), the “abstraction process” is the “most important and high-level thought process in computational thinking”. In abstraction the focus is only on the important information, ignoring irrelevant details: “It is used to capture essential properties common to a set of objects while hiding irrelevant distinctions among them”. 

The OECD’s PILA (Platform for Innovative Learning Assessments) Computational Problem Solving Module specifies the “many levels of abstraction” that CT includes: 

“Using computation to solve problems requires the ability to think in a certain way, which is often referred to as ‘computational thinking’. The term originally referred to the capacity to formulate problems as a defined set of inputs (or rules) producing a defined set of outputs. Today, computational thinking has been expanded to include thinking with many levels of abstraction (e.g., reducing complexity by removing unnecessary information), simplifying problems by decomposing them into parts and identifying repeated patterns, and examining how well a solution scales across problems.”  (OECD 2021; emphasis added)

The importance of training students to develop skills in computational problem-solving is sheeted home to the demands of the labour market:

“Computers and the technologies they enable play an increasingly central role in jobs and everyday life. Being able to use computers to solve problems is thus an important competence for students to develop in order to thrive in today’s digital world” (OECD 2021; emphasis added) 

The need to be able to use computers is not in dispute. The suggestion that problems ought to be formulated in ways computers can solve and that this task involves simplification and decomposition, however, raises issues that need to be considered.

What critics bring to our attention

It is difficult to locate critical analyses of CT. The vast majority of articles express enthusiasm about the development of computer skills and “computational thinking”.  For example, Ogegbo and Ramnarain (2022: 203) state that “CT is acclaimed as an approach to teaching the essential 21^st-century skill set that every student requires to thrive in the changing world”. Belmar (2022: 1) describes computational thinking as “the fundamental skill for scientific, technological, and economic development for the twenty-first century”. He acknowledges, however, that “this diagnosis that is unveiled in rich countries remains nebulous for poor countries”.

Kafai et al. (2020) identify three theoretical approaches to CT: cognitive CT, situated CT and critical CT. Critical CT “proposes an analytical approach to the values, practices, and infrastructure underlying computation”, reminding us that “computing is not an unequivocal social good”. In his thought-provoking commentary on technological/digital innovation King (2023: 105, 108) expresses concern about the way in which computational thinking comes to mean (simply) the sorting of “information”, and how a “computational theory of mind” overlooks the contextual and the social (King 2023: 105, 108).

Williamson’s (2016: 42) work on the place of “learning to code” in the UK K-12 curriculum draws upon a governmentality perspective to highlight the influence of “networks between governmental, civil society and commercial actors”. A particular focus is the subjectification effects of proposals to include “learning to code” in K-12 curricula. Says Williamson, through such programs, CT shapes students’ digital subjectivities, preparing them as: 

“the ideal participants for the ‘digital governance’ of the reluctant state, as citizens with the technical skills, computational thinking and solutionist mindsets to ‘hack’ solutions to problems of contemporary governance on behalf of the government.” (Williamson 2016: 54)

Importantly Williamson describes CT as a “way of thinking” or a “style of thinking” that extends beyond the classroom. He characterizes this way of thinking as politicalcomputational thinking: 

“Political computational thinking is a style of thought, then, that aims to translate social phenomena into computational models that can then be solved by being formalized as step-by-step algorithmic procedures that can be computed as proxies for human judgment or action.” (Williamson 2016: 40; emphasis added)

This general style of governing “takes technological change as the model for political innovation” (Barry 2001: 2 in Williamson 2016: 40). 

In Williamson’s account, the downsides of approaching governing through CT, with its simplifying practices of abstraction and decontextualization, are stark. The focus shifts from people’s lives to things that can be measured and tabulated. As one contemporary example illustrating this point, in the recent robo-debt tragedy in Australia, the decision to use income averaging to identify those with welfare debts ignored the fact that the targeted population frequently did not work standard full-time hours (Whiteford 2023). As Williamson (2016: 54) concludes:

“This emerging solutionist state is one in which political computational thinking, based on a technocratic logic that all social phenomena can be formalized into computable models, has become the main governmental style of thought”.

The missing link: problem-solving

To understand what is going on with CT both as an educational innovation and as a style of governmental thinking requires attention to the underlying premise of these approaches: that living and governing rely on problem-solving. Even our most astute critics of CT appear to bypass the way in which a CT style of thinking and governing starts from the premise that there are problems, pure and simple, that need to be solved. Despite his careful reflections on the constitutive role that technology plays in human affairs, King (2023: 63, 104) is happy to claim that human beings are “problem-solving creatures, after all”.

Williamson highlights the importance of thinking about “styles of thought” in governing. He elaborates that “styles of thinking” can be characterized as “particular ways of thinking, practicing, making explanations, conceptualizing problems and proposing solutions in a given professional or disciplinary community” (Williamson 2016: 40; emphasis added). I have italicized the words “conceptualizing problems” because it is this aspect of CT that, in my view, goes underexamined.

The literature on CT tends to portray its role as a “problem-solving approach” as simple common-sense. The intrinsic connection between CT and problem-solving appears in the definition offered at the beginning of this entry and repeated here: “In this context, computational thinking refers to formulating problems and designing their solutions in a form that can be executed by or with a computer (Cuny, Snyder and Wing 2010).” (OECD 2019: 6.1.1 para 168; emphasis added). Even Papert, who ought to be acknowledged as pioneering in his questioning of the ways in which computers are conventionally used for drill and practice, still worked within a problem-solving mindset. With Faurzeig, he (2011: 490) describes how the “activity of programming” “fosters an experimental approach to solving problems”. In the dominant iteration, associated with Wing (2010: 1), computational thinking “describes the mental activity in formulating a problem to admit a computational solution”.

On the one hand the reference to “formulating problems” appears to endorse a more open-ended meaning of “problem” – since “problems” can be made (formulated). However, the fact that formulation depends on a single criterion, to make the “problem” amenable to computer analysis, imposes severe constraints on what is considered to be relevant. No space exists to interrogate “problems” in terms of underlying presuppositions, genealogy or effects (Bacchi 2009).

Moreover, the “five basic concepts” needed to produce computational thinking as a “viable problem-solving approach” all refer to problems simply as existing states or conditions:

• Decomposition – This involves breaking down a complex task into smaller, and more manageable components [commonly described as “problem decomposition”, Grover and Pea 2013: 2); 
• Recognition of pattern – This involves identifying and defining trends or patterns within a problem; 

• Abstraction, which involves identification of particular similarities and differences between comparable problems to work towards a solution; 
• Algorithm design, which involves the development of step-by-step guidelines for solving a problem and can be used again to answer similar problems; and 
•  Automation, which involves the use of technological tools to mechanise problem solutions. (Ogegbo and Ramnarain 2022: 204-205).

I have italized terms in this description that indicate how each designated CT “concept” relates to and depends upon a conception of “problems” as pre-existent and waiting to be addressed/solved.

Some might want to make the case that the emphasis in CT on mathematical-type problems means that analyses of their underlying presuppositions, genealogy and effects, are simply irrelevant because (after all) mathematical problems are pretty clear-cut. However, even within mathematics, there is dispute about the very nature of problem-solving and the meaning of “problems” (Bacchi 2020: 85). Moreover, Wing (2010: 1; emphasis added) argues that CT targets not just “mathematically well-defined problems” but also “real-world problems”: 

“When I use the term computational thinking, my interpretation of the words “problem” and “solution” is broad; in particular, I mean not just mathematically well-defined problems whose solutions are completely analysable, e.g., a proof, an algorithm, or a program, but also real-world problems whose solutions might be in the form of large, complex software systems. Thus, computational thinking overlaps with logical thinking and systems thinking.”

It follows that, despite the emphasis on problem formulation, in CT, problems, whether mathematical or “real-world”, are conceptualized as pre-existing states or conditions. They exist out there somewhere in the world to be found and decomposed “into as many parts as may be necessary for finding the solution” (Derrida 1967/1978: 287 in St Pierre and Jackson 2014: 715). 

None of this is really surprising given the ubiquity of the problem-solving paradigm, which dominates the intellectual and policy landscape. Elsewhere (Bacchi 2020) I describe the prevalence of problem-solving knowledge across a wide array of disciplines and in everyday usage in contemporary western societies and international organizations. Problem-solving knowledge is used in the French sense of savoir as a form of background knowledge or “truth” discourse. I describe how the presumption of pre-existing “problems” in problem-solving knowledge removes from consideration how “problems” are produced and represented, limiting political debate and regulating political subjects.

Usefully, Williamson (2016: 54) alerts us to the ways in which political computational thinking produces a “solutionist mindset” and a “solutionist state” – “a technocratic logic that all social phenomena can be formalized into computable models”. Troeger et al. (2023: 95) note that the software development process is often subject to a problem-solving paradigm. The literature on software development and design assumes that there “are” problems that can be identified and understood.

To step outside a “solutionist mindset” requires challenging the presumption of pre-existing problems. To this end I recommend bringing WPR into the picture – into classrooms, into board rooms, into parliamentary committees. WPR postulates a contrasting and contesting “style of thinking”. It alerts us to the depoliticizing effects produced by treating “problems” as if they simply exist waiting to be solved. To think critically about CT requires challenging the problem-solving knowledge it relies upon and promotes. Perish the thought that we should all end up “thinking” like ChatGPT!  

[Guide to usage:  Readers may have noticed some unevenness in my use of quotation marks around problems. I exclude quotation marks when the term is used in relation to a perspective that accepts problems as pre-existing entities. I have also adopted the use of a hyphen in problem-solving whether it operates as a noun or as an adjective. I have done so to signal that, in its pervasiveness, problem-solving knowledge operates as an event, in the Foucauldian sense, “the interweaving of relations of power and domination” (Tamboukou, 1999: p. 207): “Events are not simply bits of data; they need to be analyzed in terms of the practices that give rise to them” (Bacchi & Goodwin, 2016: p. 46). The hyphen in “problem-solving” is excluded when the term so appears in other publications.] 

References

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

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

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

Barry, A. 2001. Political Machines: Governing a Technological Society. London: Athlone Press. 

Belmar, H. 2022. Review on the teaching of programming and computational thinking in the world. Frontiers in Computer Science, DOI 10.3389/fcomp.2022.997222 

Cuny, J., Snyder, L. and Wing, J. 2010. Demystifying computational thinking for non-computer scientists. https://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf

Derrida, J. 1978. Writing and difference (A. Bass, Trans.). Chicago, IL: The University of Chicago Press. (Original work published 1967)

Feurzeig, W. and Papert, S. A. 2011. Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5): 487-501.  

Grover, S. and Pea, R. D. 2013. Computational Thinking in K-12: A Review of the State of the Field. Educational Researcher, 1-6. DOI: 10.3102/0013189X12463051

Kafai, Y. B., Proctor, C., & Lui, D. 2020. From theory bias to theory dialogue: Embracing cognitive, situated, 

and critical framings of computational thinking in K-12 CS education. ACM Inroads, 11(1), 44–53. https://doi.org/10.1145/3381887.

Lodi, M. and Martini, S. 2021. Computational Thinking, Between Papert and Wing. Society & Education, 30: 883-908. https://doi.org/10.1007/s11191-021-00202-5 

OECD 2019. PISA 2021 ICT Framework. OECD 2019.

OECD 2021. PILA Computational Problem Solving Module. https://pilaproject.org/guides/computational-problem-solving-framework

Ogegbo, A. A. & Ramnarain, U. 2022. A systematic review of computational thinking in science classrooms Studies in Science Education, 58(2): 203-230. DOI: 10.1080/03057267.2021.1963580 

Papert, S. 1980. Mindstorms: Children, Computers, and Powerful Ideas. NY: Basic Books.

Rose, N. 1999. Powers of Freedom: Reframing Political Thought. Cambridge: Cambridge University Press.

Shute, V. J., Sun, C. and Asbell-Clarke, J. 2017. Demystifying Computational Thinking. Educational Research Review, 22, 142-158. 
https://doi.org/10.1016/j.edurev.2017.09.003

St. Pierre, E. A. and Jackson, A. Y. 2014. Qualitative Data Analysis after Coding. Qualitative Inquiry, 20(6): 715-719.

Tamboukou, M. 1999. Writing Genealogies: An Exploration of Foucault’s Strategies for Doing Research. Discourse: Studies in the Cultural Politics of Education, 20, 201-217. https://doi.org/10.1080/0159630990200202

Tedre, M. and Denning, P. J. 2016. The Long Quest for Computational Thinking. Proceedings of the 16th Koli Calling Conference on Computing Education Research, November 24-27, 2016, Koli, Finland: pp. 120-129. 

Troeger, J., Zakharova, I., Macgilchrist, F. and Jarke, J. 2023. Digital ist besser!? – Wie Software das Verständnis von guter Schule neu definiert. In A. Bock et al. (eds) Die datafizierte Schule (The Datafied School), https://doi.org/10.1007/978-3-658-38651-1_4 

Whiteford, P. 2023. “Income averaging” lacked basic common sense. The Conversation, 16 March. 

Williamson, B. 2016. Political computational thinking: policy networks, digital governance and “learning to code”. Critical Policy Studies, 10:1, 39-58, DOI: 10.1080/19460171.2015.1052003 

Wing, J. 2006. Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10. 

1145/1118178.1118215.Wing, J. M. 2010. Computational thinking: What and why? Retrieved from http://www.cs.cmu.edu/∼CompThink/resources/TheLinkWing.pdf.

Many of us are exploring the dazzling displays of text composition offered by ChatGPT.  People I know have used it to produce mission statements and program guidelines. I became curious to see how well it might cope with questions that emerge from WPR thinking. I found some surprises, which I elaborate below. In this entry I am restricting my comments to experiments with ChatGPT-3.5.

A plethora of online and published sources are pointing to limitations and lapses in the kinds of replies ChatGPT produces. For example, ChatGPT admits a “knowledge cutoff in September 2021” (see response below). Hence, should you be looking for up-to-date material on (say) medical interventions, you need to look elsewhere.

More disturbing is the tendency for ChatGPT to make things up in replies. That is, it fabricates real-sounding information, which is certainly a concern for students, researchers and others (Botpress Community 2023). A US judge fined two New York lawyers for including five fictitious case citations in their legal brief. The firm’s statement argued that “We made a good faith mistake in failing to believe that a piece of technology could be making up cases” (Reuters in Melbourne Age, 24 June 2023, p. 26). What can I say? Believe it!

I learned about this particular ChatGPT idiosyncrasy the hard way. In the context of the three recent Research Hub entries on COVID-19 mortality rates (30 May, 29 June, 30 July 2023), I asked ChatGPT (12 June 2023): “Is it possible to specify the social and environmental causes of human mortality on the standardised international medical death certificate?” 

Here is the reply in full: “As of my knowledge cutoff in September 2021, the International Form of Medical Certificate of Cause of Death (ICD-10) does not provide specific codes to capture social and environmental causes of mortality. The ICD-10 focuses primarily on capturing medical and pathological causes of death. However, efforts have been made to expand the coding system to include these factors in the future.

To address this limitation, the World Health Organization (WHO) developed the Social Determinants of Health (SDH) Mortality Reporting Form. This form allows for the inclusion of social and environmental factors contributing to mortality. It collects information on various dimensions such as education, occupation, living conditions, and lifestyle factors.

While the SDH Mortality Reporting Form is not currently integrated into the standard International Medical Death Certificate, it provides a framework for capturing important non-medical causes of death. It serves as a valuable tool for data collection and analysis to better understand the social and environmental determinants of health outcomes.”

References (as provided with the reply):

1. World Health Organization. (2016). Social Determinants of Health Mortality Reporting Form: Implementation Guide. Retrieved from 

Click to access 9789241510109_eng.pdf

• World Health Organization. (2016). International Form of Medical Certificate of Cause of Death. Retrieved from https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf

This reply came as quite a surprise given that I have been researching the topic for several months and was unaware of the SDH Mortality Reporting Form. Diligent researcher that I am, I pursued the matter. I asked ChatGPT: “What is the World Health Organization (WHO) Social Determinants of Health (SDH) Mortality Reporting Form? Where can I access a copy?”

The reply arrived: “As of my last knowledge update in September 2021, there was no specific World Health Organization (WHO) Social Determinants of Health (SDH) Mortality Reporting Form available.”

Indeed. Needless to say, the reference previously offered to support the claim about the existence of such a form “could not be found”. 

Importantly, I was able to check on the accuracy of the claim about the existence of a SDH Mortality Reporting Form because I had researched extensively in the field. As Ian Bogost (2022) notes, this situation creates a dilemma: “If one already needs to possess the expertise to identify the problems with LLM-generated text, but the purpose of LLM-generated texts is to obviate the need for such knowledge, then we’re in a sour pickle indeed”. 

My target for today is a related “sour pickle”. I intend to use WPR to put in question the general characterization of ChatGPT as “left-leaning” or “progressive” in its political “views”. Numerous articles set out to illustrate this “bias”. Jeremy Baum and John Villasenor (2023), from the Brookings Institute, found that “for certain combinations of issues and prompts, in our experiments ChatGPT provided consistent – and often left-leaning – answers on political social issues”. David Rozado, writing in Social Sciences (2023), concurs that ChatGPT answers manifested “a preference for left-leaning viewpoints”. The near consensus on this viewpoint so disturbed Elon Musk that he plans to build “TruthGPT”, a competitor to OpenAI’s ChatGPT, which he describes as displaying a “woke” bias (WIRED 2023). 

I intend to challenge this characterization of ChatGPT as “left-leaning”. In making this challenge, one needs to consider just what “left-leaning” implies and whether it is possible to draw such sharp left-right distinctions in political orientation. Through asking WPR questions, I discovered that replies to my questions tended to be liberal/neo-liberal in character rather than “left-leaning”, as I proceed to demonstrate. To make this case I draw on the ability of ChatGPT to hold “conversations”, making it possible to “conduct a structured dialog” and to cause ChatGPR to take a position on political issues. The targeted issues are “childhood obesity” and “Aboriginal deaths in custody”. 

I should note that ChatGPT states that it refuses to engage in “partisan political issues” with an implication of objectivity: “As an AI language model, it’s not my place to take sides or advocate for any political position” (in Gewirtz 2023). However, the reference to “political” here is narrow indeed. Should ChatGPT take its own advice seriously on this point, it would be unable to function. I had no difficulty soliciting replies to questions about “childhood obesity” and “Aboriginal deaths in custody”, leaving us to assume that these issues are not deemed to be political.

“Childhood obesity”

My opening question reads (17 June 2023): “What kind of a problem is childhood obesity represented to be in current policy responses?”

Here is the reply in full: “Childhood obesity is considered a significant problem in current policy responses. It is recognized as a public health issue requiring urgent attention due to its detrimental impact on children’s health and well-being. Policy responses aim to address the causes and consequences of childhood obesity by implementing measures such as promoting healthy eating habits, increasing physical activity, improving school food environments, and enhancing public awareness and education on the importance of a balanced lifestyle.”

Note that ChatGPT adopts a conventional reactive view of “policy responses” as “addressing” “the causes and consequences of childhood obesity” (Bacchi 2009: 1). Hence, there is no understanding of the WPR focus on problem representations, a point I return to later. The ChatGPT answer concerning “policy responses” broadly endorses liberal and neoliberal policy measures that target individual responsibility for poor health outcomes. There is a strong focus, for example, on the need for behavioural changes, such as altering eating habits and increasing physical activity, and on promoting “a balanced lifestyle”. 

I asked a follow-up question (17 June 2023): “What about links to poverty and advertising fast food?”

ChatGPR modified its response appropriately: “Childhood obesity is closely linked to poverty and the pervasive advertising of fast food. Low-income families often face financial constraints that limit access to affordable, nutritious food options, leading to a higher reliance on inexpensive, calorie-dense foods. Fast food advertising specifically targets children, creating an obesogenic environment that promotes unhealthy eating habits. Current policy responses recognize these connections and aim to address them by implementing strategies such as promoting affordable healthy food options in underserved areas, implementing restrictions on unhealthy food advertising to children, and advocating for comprehensive policies that address both poverty and the influence of marketing on children’s food choices.”

With the “challenges” provided by my supplementary question, ChatGPR moves in a more structural direction in its analysis. Poverty, for example, now gets a mention. I decided to push the issue further. 

I ask: “What about wealth redistribution?”

The reply reads: “Wealth redistribution is not typically considered a direct policy response to childhood obesity. While addressing income inequality and promoting economic equity are important societal goals, policy responses to childhood obesity tend to focus on specific interventions related to nutrition, physical activity, education, and public health. However, addressing poverty and improving access to resources, including nutritious food options and healthcare, can indirectly contribute to reducing childhood obesity rates. By addressing socioeconomic disparities, policies that aim for wealth redistribution can create an environment that supports healthier lifestyles and equitable opportunities for all children, thus indirectly impacting the prevalence of childhood obesity.” 

I got the distinct feeling from this reply that ChatGPT was “uncomfortable” with an explicit reference to the possibility of wealth redistribution, though it did its best to accommodate the argument. 

The point I would like to make from this example is that it was possible to shift ChatGPT in the direction of a more structural analysis through asking explicit follow-up questions. My concern is that the kind of “conversation” I produced here is unlikely to emerge from a casual encounter with ChatGPT. To pose follow-up questions, as I did, requires a prior engagement with the literature and with contesting points of view. As with the above example of the non-existent SDH Mortality Reporting Form, those seeking a reply would be likely to accept the initial ChatGPT response without question, reinforcing the socio-political status quo. 

“Aboriginal deaths in custody”

I asked (17 June 2023): “What kind of a problem is Aboriginal deaths in custody represented to be?”

The reply in full reads: “Aboriginal deaths in custody are widely recognized as a deeply troubling and urgent problem. These deaths represent a critical issue of human rights, social justice, and systemic racism. The overrepresentation of Aboriginal people in the criminal justice system, coupled with inadequate care and response within custodial settings, has led to a disproportionate number of deaths. This issue highlights the need for comprehensive policy reforms, including culturally appropriate support services, improved police practices, and enhanced accountability mechanisms, to address the underlying factors contributing to Aboriginal deaths in custody and ensure the protection of human rights for all individuals in custody”.

My follow-up comment reads: “You didn’t mention the living conditions of Aboriginal peoples as an underlying cause.”

Reply in full: “Apologies for the oversight. The issue of Aboriginal deaths in custody is indeed intertwined with the broader socio-economic and living conditions experienced by Aboriginal peoples. These conditions, which include higher rates of poverty, limited access to healthcare, education, and employment opportunities, along with the historical and ongoing impacts of colonization, contribute to the overrepresentation of Aboriginal individuals in the criminal justice system. Addressing the systemic issues that perpetuate these living conditions is crucial for a comprehensive approach to preventing deaths in custody and achieving meaningful change in the lives of Aboriginal peoples.”

As with “childhood obesity”, ChatGPT is quite prepared to “correct” itself when challenged to do so. Again, as with “childhood obesity”, there is a shift towards more structural explanations. 

How has this development occurred? 

I am tempted to ask: “Where have the ‘views’ in the initial replies come from?”  Here we move into the practical processes involved in generating ChatGPT. Rozado (2023) explains that “Like most LLMs [large language models], ChatGPT was trained on a very large corpus of text gathered from the Internet”. Such a corpus “would be dominated by influential institutions in Western society”. These institutions, argues Rozado, would include prestigious universities, where “the majority of professionals working in those institutions are politically left-leaning” (Rozado 2023). In addition, a team of human labelers is embedded in the training loop of ChatGPT and their “biases” might “have percolated into the model parameters” (Rozado 2023). While I dispute Rozado’s argument that the “views” he identifies are “left-leaning”, these explanations about sources of “bias” may provide insights into the pervasiveness of the liberal/neo-liberal orientation I identify in ChatGPT’s responses.

My “conversations” with ChatGPT involved application of WPR thinking. My supplementary questions (above) displayed an attempt to probe practices that are usually silenced (Question 4 in WPR; see Bacchi and Goodwin 2016: 20). Through asking these questions I was able to “force” ChatGPT to take a more structural position. However, as mentioned previously, it is possible to intervene in this fashion only if a person is already thoroughly familiar with the contesting positions around a specific issue. Generally, the first response produced by ChatGPT will be put forward in essays and articles as “accurate” and “complete”. 

I had attempted to get ChatGPT to engage with the notion of problem representations in my initial query: “What kind of a problem is childhood obesity represented to be in current policy responses?” and in the second question “What kind of a problem is Aboriginal deaths in custody represented to be?” In the last section below I consider how well ChatGPT performed in understanding and engaging with this theoretical stance. In brief, the answer is – not very well. 

ChatGPT and political theory

In reply to the first question “What kind of a problem is childhood obesity represented to be in current policy responses?” ChatGPT replied that: “Childhood obesity is recognized as a significant problem in current policy responses.” Here the term “problem” is treated as an unproblematic concept, as something that simply exists, with no consideration of how the “problem” is represented. The reply then states that “childhood obesity” is “viewed as a multifaceted health challenge”, again with no consideration of the implications of this problem representation. And, as mentioned previously, the reference to “policy responses” as “addressing” “the causes and consequences of childhood obesity” reflects a conventional view of policy processes as reactions to “problems-that-exist”. 

Similarly, in response to the question “What kind of a problem is Aboriginal deaths in custody represented to be?”, ChatGPT replies that “Aboriginal deaths in custody are widely recognized as a deeply troubling and urgent problem”. Again, “problems” are treated as pre-existent and fixed. In an attempt to respond to the wording in the initial WPR question about how the “problem” of Aboriginal deaths in custody is represented, ChatGPT has this to say about “representation”: “These deaths representa critical issue of human rights, social justice, and systemic racism (emphasis added)”. This reply shows no understanding of what is involved in problem representation (see Bacchi and Goodwin 2016).

This exchange illustrates the limitations of ChatGPR when dealing with concepts and theory. I decided to test this ability further and discovered some surprising findings.

I asked ChatGPT two direct questions about “problems” and WPR thinking. 

My first question reads (17 June 2023): “Is there a need to question how Computational Thinking conceptualises problems as pre-existing states?”

The reply reads: “Yes, there is a need to question how Computational Thinking conceptualizes problems as pre-existing states. By assuming that problems are static and predefined, we may overlook the dynamic and context-dependent nature of real-world problems. This perspective limits our ability to adapt and find creative solutions. It is crucial to explore alternative problem-solving approaches that emphasize problem formulation, reframing, and iterative problem-solving processes”.

I had not expected a “yes” reply nor the explicit questioning of “problems” as “static” and “predefined”. At the same time, there is an endorsement of the need to attend to “real-world problems” and to explore “alternative problem-solving approaches” – both positions questioned through WPR thinking. 

My second question reads (17 June 2023: I requested references): “What does it mean to say that policies constitute problems as particular sorts of problems?”

Here is the reply: “To say that policies constitute problems as particular sorts of problems means that policies define and frame problems in a specific way, shaping how they are understood and addressed. Policies determine the boundaries, scope, and parameters of a problem, as well as the preferred approaches for solving it. By defining problems through policy lenses, certain aspects may be emphasized while others are overlooked or marginalized. This framing influences the allocation of resources, the formulation of solutions, and the distribution of benefits and burdens. References:

1. Bacchi, C. (2009). Analysing policy: What’s the problem represented to be? Pearson Education Australia.
2. Howlett, M. (2011). Designing public policies: Principles and instruments. Routledge”.

This reply would satisfy many people working with WPR. However, there is slippage around the use of the term “problem”. See, for example, the references to “parameters of a problem”, and “for solving it”, as if the problem is a pre-existent entity. There is also an emphasis on problem definition, which is associated with an interpretive mode of analysis, as opposed to the focus in WPR on how “problems” are produced (or constituted) as particular sorts of problem (see Bacchi 2015: 5). While my specific questions in this case elicited some useful statements, the argument is both vague and inconsistent. 

These replies indicate two important characteristics of ChatGPT-generated text. First, ChatGPT responses reflect closely the wording in the question, as happened when I asked specifically about how “problems” are constituted as particular sorts of problem. As Baum and Villasenor (2023) note, 

“seemingly small changes in how a prompt is constructed can lead to very different responses. This is because AI-powered chatbots identify which data to draw from in a manner that is highly sensitive to the specific phrasing of the query”. 

And second, as a result of the first point, you will get what you ask for. The difficulty arises when you do not know what you ought to be asking for. ChatGPT will only reflect on what is asked of it – thinking has to precede the decision to use it! 

As my colleage Angelique Bletsas puts it, in her inimitable and insightful precis of the argument: 

“ChatGPT can summarize existing material – that which it has access to – and that to which it is directed – but that is a very different kind of activity to actual analysis. So what we understand research to involve is at stake in the move to adopt these technologies without first coming to terms with their design limitations. If you had to give a research assistant as much direction (or “prompting”) as you had to give ChatGPT, you’d quite likely be looking for a better research assistant!”

References

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

Bacchi, C. 2015. The Turn to Problematization: Political Implications of Contrasting Interpretive and Poststructural Adaptations. Open Journal of Political Science, 5: 1-12. 

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

Baum, J. and Villasenor, J. (2023). The politics of AI: ChatGPT and political bias. Brookings, 8 May. (https://www.brookings.edu/blog/techtank/2023/05/08/the-politics-of-ai-chatgpt-and-political-bias/

Bogost, I. 2022. ChatgGPT is Dumber Than You Think. The Atlantic, 7 December.https://www.theatlantic.com/technology/archive/2022/12/chatgpt-openai-artificial-intelligence-writing-ethics/672386/

Botpress Community 2023. How Accurate is ChatGPT in Providing Information or Answers? https://botpress.com/blog/how-accurate-is-chatgpt-in-providing-information-or-answers#:~:text=As%20an%20AI%20language%20model,provide%20incorrect%20or%20inaccurate%20answers

Gewirtz, D. 2023. 6 things ChatGPT can’t do (and another 20 it refuses to do). ZD Net: https://www.zdnet.com/article/6-things-chatgpt-cant-do-and-another-20-it-refuses-to-do/

Rozado, D. 2023. The Political Biases of ChatGPT. Social Sciences 12: 148. https://doi.org/ 10.3390/socsci12030148WIRED 2023. Meet ChatGPT’s Right-Wing Alter Ego. 27 April. (https://www.wired.com/story/fast-forward-meet-chatgpts-right-wing-alter-ego/)

A few months ago I suggested it was time to apply WPR to WPR. I have thought of undertaking this project on numerous occasions. I was prompted into action by several WPR applications that have picked up the suggestion to apply WPR to contrasting theoretical perspectives (Bacchi 2009: 128-136; 103-105; 249-251; Research Hub entry 18 March 2018).

For example, Primdahl et al. (2018) reflect on the forms of critical analysis produced in some selected articles contributed to the Journal of Curriculum Studies. To undertake an analysis of the “content of the argumentation”, the authors examine the various contributions in terms of their problematizations, in effect applying WPR to the selected articles (see Research Hub entries 3 December 2018; 17 Dec. 2018). More recently, Johansson and Larsson (2022) use WPR to produce a critical review of the research literature on identity in university physics. These examples highlight the usefulness of applying WPR to theoretical propositions, opening up the opportunity to apply WPR to WPR. 

The decision to apply WPR to WPR was also motivated by the recognition that, as researchers, we are inside the processes we are examining. In line with the so-called “reflexive turn” in poststructuralist critique, it is necessary, therefore, to acknowledge that the researcher/theorist plays an active role in constructing the very reality s/he is attempting to investigate (Eveline and Bacchi 2010: 154). It is time, therefore, to ask myself just what “reality” WPR creates. 

What does WPR propose?

Applying WPR involves selecting specific proposals to gain access to the problematizations at work in the analytic target, in this case WPR (Research Hub entry 30 Jan. 2023). I decided to use my 2012 article “Introducing the ‘What’s the Problem Represented to be?’ approach” for this purpose since, by its nature (short, introductory), it is both simple and clear (Bacchi 2012: 21-24). By the same token, given its brevity, it lacks nuance and depth. I have always prided myself on an ability to “reduce” complex theory to accessible prose. Today, I remind myself that “pride cometh before a fall”! On this note I apologize in advance for the many references to my publications, which became inevitable in this attempt to problematize WPR. 

The short, simple Introduction to WPR drafted in 2012 reminded me of a critical article I wrote on a 2001 directive from the Canadian Health Services Research Foundation to health researchers to simplify their analyses to make them “useful” to policymakers. In this directive researchers are:

“Instructed to use plain English and to avoid jargon; they are to lay out their research using a 1:3:25 format, with one page of ‘main messages’, three pages of ‘executive summary’ and twenty-five pages for the main report.” (in Bacchi 2008: 169-170)

In my article on this directive I make the point that condensing the analysis to fit on one page potentially distorts the content. In support of this proposition, Mills and Thomson (2023: 193) describe how the sociological perspectives they, as consultants, sought to bring to the analysis of exclusion in UK education policy “were side-lined in the executive summary”. Clearly, then, the format we adopt to “convey” our “messages” – and this applies equally to my short Introduction to WPR – have power effects, which ought to be acknowledged.

In this Research Hub entry I treat the WPR set of questions (Bacchi and Goodwin 2016: 20) as a “practical text” and a guide to “conduct” (Bacchi 2009: 34). The brief Introduction, just mentioned (Bacchi 2012: 21), describes WPR as a “resource, or tool, intended to facilitate critical interrogation of public policies”.  Readers are informed that “this task is accomplished through a set of six questions and an accompanying undertaking to apply the questions to one’s own proposals for change”. While the question format suggests that the approach is not prescriptive, there is still the implication that WPR produces an organized way to proceed that ought to be followed. The power implications of this form of analysis need to be recognized. 

These power implications are most obvious in what Sue Goodwin and I (2016: 20) now refer to as “Step 7”, which calls upon researchers to engage in self-problematization: “Apply this list of questions to your own problem representations”. In contrast to the other six questions in the approach, “Step 7” is not a question but an instruction. Over the years I have struggled to find a term to soften the blow, if you will, making Step 7 more of a recommendation than a command. You will find the “instruction” described in different places as a “directive” (Bacchi 2009: 19; judged to be too strong), an “injunction” (blurb on back cover of Bacchi 2009; still too strong), an “undertaking” (Bacchi and Goodwin 2016: 19; just right?). I feel a little bit like Goldilocks! 

An undertaking is described as a task that is (simply) taken on, though it also carries the implication of a formal promise (Cambridge Dictionary). I judge the term to be “just right” since it appears to throw the ultimate decision to engage in self-problematisation, which is what Step 7 entails, back onto the researcher. You can see here my struggle to balance the power effects of WPR with a determination to encourage self-problematization. In the most recent iteration of the WPR “method”, I decide that encouraging self-problematisation ought to be the priority, for reasons elaborated later:

“In terms of practical application of WPR, it is possible to draw selectively upon the forms of questioning and analysis just described, so long as a self-problematizing ethic is maintained” (Bacchi and Goodwin 2016: 24; emphasis added). 

What is produced as “the problem”?

Given that the proposal is to adopt the WPR questions as a guide to research, the “problem” is produced as alternative ways of thinking (e.g. positivism). As a result, WPR is automatically engaged in epistemological and ontological debates. Following Question 2 in WPR, I ask: which epistemological and ontological presuppositions underlie the identified problem representation in WPR?

In several places (e.g., Bacchi and Goodwin 2016: 15, 33, 49) I, with Sue Goodwin, specify that WPR is associated with an ontology of becoming rather than an ontology of being. A clear distinction is drawn with realist premises. In an ontology of becoming, assumptions about the being of “things” are replaced by references to their becoming (Chia 1996). The physicality of “objects” is not questioned. However, “objects” are seen as in continuous development, as “in formation”, rather than as fixed. In effect, practices and relations replace “objects” (Veyne 1997). “Things” become open-ended and malleable. The argument is that such an ontological stance creates space for contestation and unmaking.

“Subjects” too are considered to be in a process of continuous development. In this argument, the idea that human beings are sovereign subjects who grasp meaning intuitively (Foucault 1972: 227) is rejected. This questioning of the humanist subject is contested in many quarters.

In terms of epistemology, a particular way of thinking about “knowledge” is at work in WPR. Following Foucault, knowledge is not “truth” but what is “in the true”, what is accepted as true (Bacchi and Goodwin 2016: 31, 35). Knowledges, or discourses, are treated as contingent historical creations that produce hierarchical and inegalitarian forms of rule. The focus of analysis becomes how “truths” are produced. 

This position is best described as anti-epistemological rather than as endorsing a specific epistemology. However, WPR makes a knowledge claim in its starting premise: “what one proposes to do about something reveals what one thinks is problematic (needs to change)” (Bacchi 2012: 21). This statement/claim displays a form of logic, or correct reasoning. At the same time, WPR criticizes forms of analysis that appear to claim “truth status”. Classically, it distinguishes itself from those forms of “ideology critique” that talk about “false consciousness”, implying the possibility of a “true consciousness” (discussion below). 

How does WPR negotiate this tension between making a truth claim and questioning claims to truth? The role of Step 7 becomes critical here. Through this analytic intervention (Step 7), WPR puts into question its own knowledge claims. 

How did we get “here” from “there”?

Since it is infeasible to produce a full-blown genealogy of WPR (Question 3), I have decided simply to list what would need to be included in such a genealogy, with some indications of issues that require elaboration. 

The short Introduction (Bacchi 2012), which I am using as our starting point, indicates that WPR has changed since its incarnation in the 1999 book, Women, Policy and Politics: the construction of policy problems (Bacchi 1999). Questions 3 and 6 were added in 2009 (Bacchi 2009: 2). Moreover, in 2012, I emphasized the need to refer to the approach as “What’s the Problem Represented to be?” rather than “What’s the problem?”, which I had used in 1999. To capture the fluid nature of WPR – how it changes over time –  the 2012 Introduction describes the “WPR” approach as an “open-ended mode of critical engagement, rather than as a formula”.

A genealogy would need to probe the alterations made to WPR mentioned in the 2012 Introduction and subsequent amendments to the approach to see what they reveal about the influences affecting the development of WPR. To my knowledge only one scholar has undertaken this task, Daniel Chao (2019), and he contributes significantly to locating WPR theoretically in terms of its shifting iterations. 

As one example, in the 1999 book I talk about WPR as a study of competing interpretations of “problems”, a perspective supported in the use of the term “construction” in the title – “the construction of policy problems” (Bacchi 1999: 2). The use of that term reflects the reliance at that time on social construction theorists (Bacchi 1999: 52-57).

By contrast the 2012 brief Introduction (p. 22) describes policies as producing “‘problems’ with particular meanings that affect what gets done or not done, and how people live their lives”. This move away from social constructionism and interpretivism to a focus on the constitutive effects of problem representations is strengthened in subsequent iterations. In my keynote address at the Karlstad Symposium I characterized this shift as a move away from social constructionism towards embracing performativity (see  KEYNOTE ADDRESS – CAROL BACCHI – 18 August 2022). 

The shift in WPR from a constructionist to a performative theoretical stance indicates the need to include a genealogical survey of critical theories through the period of writing. I would ask: which authors are identified as contributing to the thinking in WPR and what are their theoretical commitments? 

Clearly, Foucault becomes a major focus. While he is not explicitly mentioned in 2012, the articles referenced in the brief Introduction include one with Jennifer Bonham on “discursive practices”, a key concept in Foucault (2014), and one with Malin Rönnblom, on “discursive institutionalism” (2014), which contrasts a Foucauldian approach to other theoretical stances on “institutions”. 

Foucault clearly increases in influence in the evolution of WPR. The 2009 book, Analysing Policy: What’s the Problem Represented to be?, contains numerous quotes from Foucault in shaded boxes, from early in the text, to illustrate specific theoretical points (Bacchi 2009: xv, xix, 17, 26, 27, etc.). By 2016, with Sue Goodwin, I describe WPR as “a Foucault-influenced poststructural approach” (Bacchi and Goodwin 2016: 28) and note the importance of identifying “distinctions from other intellectual traditions”. The next genealogical task would entail identifying those distinctions.

Such a task immerses the researcher in the theoretical debates that have engaged those on the left politically from at least the time of Marx and Engels. It would involve developing a familiarity with the Frankfurt school, with Gramsci, with Laclau & Mouffe, with Latour, with Žižek, and so on. It would also necessitate clarifying the distinction between social constructivism, social constructionism and performativity (Bacchi 2009: 32-33).  

The debates around “ideology critique” and “false consciousness” help to locate WPR thinking in terms of theoretical developments (Research Hub 30 Sept 2021; 30 October 2021). The issue of concern became how to “understand how relations of domination or subordination are reproduced with only minimal resort to direct coercion” (Purvis and Hunt 1993: 474). Where Marx and Engels target the “distorted beliefs intellectuals held about society and the power of their own ideas” (Eyerman 1981: 43), post-Marxists, including Gramsci, Althusser and the early Frankfurt School (Daldal 2014: 157) are more concerned with the “false consciousness” of the working class. In the latter explanation, “false consciousness” serves as a convenient explanation “for the reluctance of oppressed workers to rise in revolt” (Strickland 2012: 48).

Displaying its Foucauldian sympathies, WPR questions the diagnosis of “false consciousness”, which presumes the ability to identify “true consciousness”. The pivotal place of self-problematization in WPR, indicated in Step 7, counters any such impression of epistemic privilege. In Foucauldian-influenced analyses, such as WPR, there is a shift in focus from the grand theorizing of a force called ideology to the minutiae of routine and mundane practices (Bacchi and Goodwin 2016). What is at stake here are contrasting versions of political change, as John Law (2008: 637) explains: 

“It is to refuse to be overawed by seemingly large systems, and the seeming ontological unity of the world enacted by large systems. It is, instead, to make the problem smaller, or better, to make it more specific.”

Disputes over the adequacy of this diagnosis/prognosis lie at the heart of many contemporary theoretical debates. 

What remains unproblematized in this problem representation?

Numerous scholars have pointed to topics and perspectives that they argue ought to be included in WPR. They often deal with these perceived lapses by combining WPR with other theoretical approaches. A central concern is that the focus on “routine and mundane practices” (see above) precludes critiques of large and patterned forces of structural oppression (Van Aswegan et al. 2019: 195). Another complaint is that the decentred subject in Foucauldian-influenced forms of analysis denies actors’ agency and bypasses social actors’ “everyday life and practice” (Brady 2014: 27). 

Questions are also raised about how WPR engages with important theoretical debates around feminism, disability, racism, ethnicity, sexual orientation and post-coloniality. The 2009 book identifies “feminist body theory” as a key intellectual tradition in WPR (Bacchi 2009: 264). No such reference appears in the 2016 book, though the text reflects on the engagement between poststructuralism and feminism (Bacchi and Goodwin 2016: 10-11, 42) and considers how “gender equality” is produced as a particular sort of “problem” in specific sites (pp. 65, 103). Researchers have productively explored the need to combine WPR with Critical Race Theory (Street et al. 2021), Critical Disability Theory (Van Aswegan et al.,2019), Critical Sexuality Studies (Pienaar et al. 2018; Manlik 2020), and post-colonial theory (Dixit and Banday 2022). 

What effects accompany this problem representation?

In the reluctance to address structural forms of oppression, WPR is considered by some to be involved in narrowing the terms of reference of critical analysis in unproductive ways. There is also widespread concern with the subjectification effects of the non-humanist subject presumed in WPR – specifically, the questioning of human agency. This concern translates into a conviction that poststructural forms of analysis create people as dupes of “the system”, diminishing the value of people’s lives. At a practical level, the reform implications (“lived effects”) of WPR are often considered weak, if not defeatist. The unwillingness to commit to specific reform agendas is held to undermine the possibility of progressive change (Savage et al.2021). 

Poststructuralists themselves express hesitation about the practical effects of reflexive research practices. Since meaning in poststructuralism can be fixed only temporarily, Chia (1996: 49) insists that “our own theoretical products must be self-deconstructing”. He states that “throw-away explanations are the essence of reflexive practice”, limiting the political utility of interventions such as WPR. 

Which specific practices produce this problem representation? And what forms of resistance are identifiable? (Question 6)

WPR has emerged from knowledge practices primarily in Western universities and through the contributions of a community of scholars based in these universities. Sue Goodwin and I (2016: 11) also stress the role of policy workers/analysts engaged in deploying WPR. Together, these researchers have expressed disquiet with contemporary political practices around issues to do with gender, race, disability, economic inequality, and heteronormativity.

From those who position themselves on the left politically, concern is expressed that poststructuralism weakens the political offensive against capitalism and its “vested interests”. From the right, the questioning of the rational agent is identified as a threat to human progress and Enlightenment. 

How can I apply self-problematization to an exercise in self-problematization? Step 7 – What is the point of this exercise? 

Subjecting WPR to a WPR analysis allows us to see that every research enterprise is an exercise in power relations. No such exercise is innocent. WPR encourages a particular way of thinking. That way of thinking challenges realist premises. It also puts under erasure a humanist conception of the subject. 

The extent to which these perspectives make it difficult to identify and confront groups and individuals who display intentional malfeasance in political encounters is a matter of contention. There is also a need to consider the adequacy, in terms of political response, of analysing the “conditions of emergence, insertion and functioning” of “regimes of truth” (Foucault 1972: 163). 

To apply Step 7 to this analysis, I need to consider if, as a critic, I may have missed something. Attention can be drawn to the determination in WPR to disrupt the power effects one inevitably creates (see discussion above). While the approach accepts the inevitability of “fixing” meanings, if only temporarily, it also creates space to question (“unfix”) those meanings, illustrating what Lather describes as a “doubled practice” (Bacchi and Eveline 2019: 340; Lather 2001). Researchers are prompted to see themselves as politically invested cultural beings who examine critically the analytic categories they adopt (Bacchi and Eveline 2010: 342). In this way, scepticism about the truths we produce and those we critique becomes a productive political force for change. 

As with the application of WPR in other sites, applying its questions to WPR itself ensures that an ongoing process of problematization and self-problematization is maintained. As we are all implicated in power relations and as our work necessarily has power effects, this recursive practice becomes essential.

References

Bacchi, C. 1999. Women, Policy and Politics: The construction of policy problems.London: Sage. 

Bacchi, C. 2008. The politics of research management: Reflections on the gap between what we “know” (about SDH) and what we do. Health Sociology Review, 17(2): 165-176.

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

Bacchi, C. 2012. Introducing the “What’s the Problem Represented to be?” approach. In A. Bletsas and C. Beasley (Eds) Engaging with Carol Bacchi: Strategic Interventions and Exchanges. Adelaide: University of Adelaide Press. pp. 21-24.

Bacchi, C. & Bonham, J. 2014. Reclaiming discursive practices as an analytic focus: Political implications. Foucault Studies, 17 (March): 173-192.

Bacchi, C. and Eveline, J. 2010. Conclusion: A Politics of Movement. In C. Bacchi and J. Eveline, Mainstreaming Politics: Gendering practices and feminist theory.Adelaide: University of Adelaide Press. pp. 139-161. pp. 335-343. 

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

Bacchi, C. & Rönnblom, M. (2014). Feminist Discursive Institutionalism – A Poststructural Alternative. NORA – Nordic Journal of Feminist and Gender Research, 22(3): 170-186.

Brady, M. 2014. Ethnographies of Neoliberal Governmentalities: from the neoliberal apparatus to neoliberalism and governmental assemblages. Foucault Studies, 18: 11-33.

Chia, R. 1996. The problem of reflexivity in organisational research. Organization, 3(1): 31-59.

Chao, D. 2019. Problematizaciones, problemas representados y gubernamentalidad. Una propuesta analítica para el estudio de las políticas públicas y el estado.

(Problematizations, problems represented and governmentality. An analytical proposal for the study of public policies and the state). De Prácticas y discursos Universidad Nacional del Nordeste Centro de Estudios Sociales, 8(11): 123-152. DOI:10.30972/dpd.8113807

Open access at: http://revistas.unne.edu.ar/index.php/dpd/article/view/3807/3428

Daldal, A. 2014. Power and Ideology in Michel Foucault and Antonio Gramsci: A Comparative Analysis. Review of History and Political Science, 2(2): 149-167.

Dixit, A. and Banday, M. U. L. 2022. Problematising the digital gender gap: invoking decoloniality and intersectionality for inclusive policymaking, Gender & Development, 30:3, 437-457, DOI: 10.1080/13552074.2022.2117930 

Eveline, J. and Bacchi, C. 2010. Power, resistance and reflexive practice. In C. Bacchi and J. Eveline, Mainstreaming Politics: Gendering practices and feminist theory. Adelaide: University of Adelaide Press. pp. 139-161. 

Eyerman, R. 1981. False Consciousness and Ideology in Marxist Theory. Acta Sociologica, 24(1-2): 43-56. 

Foucault, M. 1972. The Archaeology of Knowledge, and the discourse on language. Trans. Sheridan Smith. NY: Pantheon Books.

Johannson, A. and Larsson, J. 2023.  Identity Perspectives in Research on University Physics Education: What is the Problem Represented to be? In: Science Identities.

Lather, P. 2001. Working the Ruins of Feminist Ethnography. Signs, 27(1): 199-207. 

Law, J. 2008. On sociology and STS. The Sociological Review, 56(4): 623-649.

Manlik, K. 2020. Allies or at-risk subjects?: sexual minority women and the “problem” of HIV in Lesbians on the Loose, Feminist Media Studies, DOI: 10.1080/14680777.2020.1837907 

Mills, M. & Thomson, P. 2022. English schooling and little e and big E exclusion: what’s equity got to do with it?, Emotional and Behavioural Difficulties, 27:3, 185-198, DOI: 10.1080/13632752.2022.2092273

Pienaar, K., Murphy, D., Race, K. & Lea, T. 2018. Problematising LGBTIQ drug use, governing sexuality and gender: A critical analysis of LGBTIQ health policy in Australia. International Journal of Drug Policy  55: 187-194.

Primdahl, N. L., Reid, A. & Simovska, V. 2018. Shades of criticality in health and wellbeing education, Journal of Curriculum Studies, DOI: 10.1080/00220272.2018.1513568

Purvis, T. and Hunt, A. 1993. Discourse, Ideology, Discourse, Ideology, Discourse, Ideology … The British Journal of Sociology, 44(3): 473-499.

Savage, G. C., Gerrard, J., Gale, T. and Molla, T.  2021. The evolving state of policy sociology: mobilities, moorings and elite networks. Critical Studies in Education, 62(3): 306-321.  

Strickland, R. 2012. The Western Marxist Concept of Ideology Critique. VNU Journal of Social Sciences and Humanities, 28 (5E): 47-56. 

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, Australia, Race Ethnicity and Education, DOI: 10.1080/13613324.2021.2019003 

Van Aswegen, J., Hyatt, D. and Goodley, D. 2019. A critical discourse problematization framework for (disability) policy analysis: “good cop/bad cop” strategy, Qualitative Research Journal, 19(2): 185-198. https://doi.org/10.1108/QRJ-12-2018-0004Veyne, P. 1997. Foucault revolutionizes history. In A. I. Davidson (Ed.) Foucault and his Interlocutors. Trans. C. Porter. Chicago: University of Chicago Press

The Social Determinants of Health (SDH, or sometimes SDoH) have been on the health agenda for over two decades. The World Health Organization (WHO) describes them as “the non-medical factors that influence health outcomes”: “They are the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life” (WHO 2023). The literature in the area is extensive. There have been numerous WHO Commissions on the topic. The 2009 Commission expanded the definition of SDH: 

“The social determinants of health are defined as the structural determinants and conditions of daily life responsible for a major part of health inequities between and within countries. They include the distribution of power, income, goods and services, and the circumstances of people’s lives, such as their access to health care, schools and education; their conditions of work and leisure; and the state of their housing and environment. The term ‘social determinants’ is thus shorthand for the social, political, economic, environmental and cultural factors that greatly affect health status.” (WHO 2009)

These “social determinants” do not immediately come to mind when one mentions a death certificate. Rather, the tendency is to think about death certificates simply as administrative forms that must be filled in to track deaths in the community. Death rates clearly make a link more feasible – illustrating, for example, the high numbers of deaths proportionately among certain social groups, for example in suburbs with lower socioeconomic status (Glover et al. 1999; AIHW 2022). However, death certificates precede death rates. In fact, they constitute death rates. According to Swain et al. (2005: 652), “the primary tool for measuring mortality rates is the death certificate”. Hence, I suggest that analytically they deserve a closer look. 

In this analysis, death certificates are treated as governmental mechanisms or technologies that have effects “in the real” (Bacchi and Goodwin 2016: 44). Specifically, how COD (cause of death) is problematized in these administrative documents/forms shapes lives and living conditions in particular ways that need to be traced. Death certificates rely upon deep-seated ontological presuppositions about how bodies work (or don’t work) and the kind of subject that “inhabits” these bodies (see Jewson 2009). The individual is regarded as an “essentially biophysiological and neurophysiological system, which can be broken down into an unlimited number of subsystems (anatomical, cells, molecules, genes) that are functional parts of the whole” (Yadavendu 2003: 5180). Disease is considered as a malfunction of one or more subsystems. The study of disease is called pathology.

At the most obvious level the focus on pathological system failure in death certification practices completely precludes attention to SDH. There is certainly no space on the standardized international death certificate that explicitly invites the listing of the “the conditions in which people are born, grow, work, live, and age” (from above). While many articles report on errors in recording COD, this entry is not strictly concerned with coding errors (but see below for further discussion) (Adeyinka and Bailey, 2022; Morgan et al., 2022). Rather, it focusses on what is invisible on death certificates – on what is NOT counted, on what is silenced and hence on what is deemed to be irrelevant or insignificant. 

The repercussions of the lack of space for SDH on death certificates are stark, given that governments collect mortality data from these forms to plan and implement health care policy, and to allocate health and research funding (Brooks and Reed 2015: 74). Indeed, the lack of space for SDH on standardized international death certificates may help explain why health promotion and public health measures are the “Cinderella” areas of the health sector (Lewis 2005: 171). In a challenge to the limitations of US COD figures, the epidemiologists Michael McGinnis and William Foege (1993: 2211) note that the preponderance of health care expenditure “will be devoted to treatment of conditions ultimately recorded on death certificates as the nation’s leading killers”. On this topic it is relevant to acknowledge the well-established overreporting of cardiovascular deaths on death certificates (Pagidipati and Gaziano 2013; Lakkireddy et al. 2004). Attempts to create space for SDH within the parameters of current death certificates are canvassed and assessed later in the entry. There we will look at Z codes, what they facilitate in terms of reporting SDH, and what limitations they face. 

In this discussion it is important to keep an eye on the larger context and whether it is deemed to be desirable to encourage the “State” to acquire more and more information about its “citizens” and “others” (Foucault 1991). Bowker (1996:55) points out that “the ultimate cause of death” is “state-defined”:

“It was made explicit in 1932 that when there were two equal underlying causes of death (e.g., cholera and leukemia) then the cause that would be most useful to the state (in this case cholera, which was a matter of public health concern) would be taken statistically as the underlying cause.”

We appear to be caught in something of a cleft stick here – we either demand that social conditions be factored into COD decision-making processes and accept state-sanctioned surveillance, or we face exclusion from important research dollars and decision-making sites.

COVID-19, SDH and COD

The pandemic has driven home the association between SDH and COD. It has opened the floodgates on research highlighting the importance of socioeconomic conditions in COVID-19 death rates. Indeed, it has become commonplace to report that the pandemic has revealed ruptures and fractures in existing social arrangements – by class, age, disability, mental illness, gender, race, etc.

McGowan and Bambra (2022) provide a useful example. They produce a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. A large majority (n=86) of the reported studies found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. McGowan and Bambra concluded that “COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged”.  Social disadvantage is captured in the concept of “deprivation” – “which is an area measure of poverty, low income, and a reflection of the wider social determinants of health (such as housing, working conditions, unemployment, health-care access, etc.) (McGowan and Bambra 2022: e968; emphasis added). This connection between SDH and COD via COVID-19 is well-established (Chávez-Almazán et al.2022; Dalsania et al. 2022; Figueiredo et al. 2020; Samuel et al. 2021; WHO 2021; Singu et al. 2020).

The question becomes – what follows from all this research? Where are the health policy interventions that attest to the impact of these comprehensive and well-supported analyses? Historically the track record on delivering on SDH is poor. Proponents seek to find explanations for what is commonly called the “know-do” gap, referring to the gap between what we know about broad social and economic patterns in population health (SDH) and what is being done – which is very little! Explanations for this inaction tend to target the apparent “lack of will” of those with institutional authority to implement SDH insights (Harvey 2006) and their possible ideological aversion to those insights (Raphael 2006; Bambra et al. 2005). 

Alongside these explanations, I suggest the need to pay more attention to structures of governance, including funding arrangements, because of the ways these practices shape social relations, subjectivities and policy outcomes (Bacchi 2008). In the particular case at hand, I argue that the operation of death certificates as governmental mechanisms helps to explain the invisibilization or silencing of SDH. In an earlier entry (30 May 2023) I apply the WPR questions to the standardized death certificate as “practical” text. There the case is made that current death certificates leave little to no room for reflecting on the social determinants of health (SDH). This theme of “silencing” is pursued in this entry. 

What else is missing from death certificates?

While this analysis is not specifically concerned with reporting errors, as already mentioned, it wishes to highlight the absences on death certificates (WPR question 4). It is a moot point whether these should be described simply as “errors” or whether they reflect systemic political imbalances.

Importantly, in some settings, the certificates themselves are missing. For example, higher income countries record all deaths, noting the causes on the death certificates. This is not the case worldwide. The problem of unrecorded deaths is particularly acute in low- and middle-income countries (Lang 2022). As Bowker (1995: 52) describes, the international classification of diseases (ICD), developed in 1893 and with ICD-11 as the latest update (WHO 2022a), imposes an imperialist reading of disease on the rest of the world.

In terms of specific “pathologies”, while mental illness is a factor in many deaths, it is almost never listed on death certificates. Psychiatric disorders are sometimes certified on death certificates, but they are seldom selected as the underlying cause of death. Goldacre et al. (2006) report that “The majority of deaths with a certified psychiatric cause are usually omitted from official mortality statistics, which are typically based on the underlying cause alone”. As a result, explain Vigo et al.(2016), 

“mental disorders appear to only account for 0·5% of total years of life lost, because GBD (Global Burden of Disease) estimates only reflect deaths directly attributed to mental disorders recorded in death certificates (mostly due to schizophrenia and substance abuse), which leads to zero global deaths attributed to bipolar disorder, depression, and other mental illnesses”.

Stirton and Heslop (2018) raise concerns about the accuracy and reliability of MCCD (Medical Certificates of Cause of Death) for people with intellectual disability, while Rockett et al. (2011) identify a grave need to improve the sensitivity of suicide certification in most countries. 

Manoeuvring within the system

A range of interventions in the COD field indicate both disquiet with the current certification process and suggestions for how to improve it. Introduced in 1954, “multiple causes” analysis is one such development. It targets all the causes of death on the death certificate, including the underlying cause and associated causes. The Australian Institute of Health and Welfare (AIHW) signalled in 2012 that “the use of multiple causes of death data to describe patterns of mortality is becoming more prominent”. Despite widening the parameters for diagnosis of disease entities, “multiple causes” analysis does not dislodge the primacy accorded the UCOD. It also clearly stays within the biophysiological paradigm. As Armstrong (2021: 1623) points out, while medicine has opened for inspection the chain of events leading to a death, in so doing it “has affirmed its own explanatory framework around death”.

Verbal autopsy and psychological autopsy aim to compensate for the inadequacies of death certificates in certain situations (thank you to Peter Mayer for this lead). In populations lacking medical certification, the WHO (2022b) recommends the use of verbal autopsy: 

“Verbal autopsy is an interview carried out with family members or caregivers of the deceased using a structured questionnaire to elicit signs and symptoms and other pertinent information that can later be used to assign a probable underlying cause of death.”

For equivocal suicide cases Rockett et al. (2011) suggest adopting psychological autopsy methods: 

“A psychological autopsy involves in-depth review of medicolegal records and follow-back interviews with family, friends and acquaintances of a decedent to look specifically for possible antecedents of his or her suicide or possible suicide.” 

Zhu et al. (2022) draw on a registry from the Korea Psychological Autopsy Center to show connections between suicide and social determinants of health in South Korea.

As with “multiple causes” analysis, verbal and psychological autopsy aim to assist in identifying COD. They remain within the biophysiological paradigm in which a notion of causality “singles out a pathological factor” (Timmermans and Almeling 2009: 23). 

Z codes: possibilities and concerns

The most explicit attempt to bring awareness of SDH to COD analysis is the introduction in 2015 of Z codes, which form part of the ICD’s (International Classification of Diseases) classificatory schema. In Australia the ABS (Australian Bureau of Statistics 2020) receives files from hospital Registrars with information about the deaths that were registered each month. The ABS then amalgamates and checks the records, assigns “cause of death” codes to each record, validates the dataset and produces statistical output. To put it bluntly, if a “condition” does not have a code, it does not get counted. The creation of Z codes is therefore a significant development and deserves attention.

The items available as Z codes are highly variable. They appear in a summary table produced by the CDC (Centers for Disease Control and Prevention 2021). Jacobs (2021) provides a useful introduction to Z codes and his concerns about their limitations. He argues that many of the existing codes are far too generic, such as Z59.8 (low income), “which encompasses a wide array of financial issues, each requiring a distinct solution”. He also points out that, in the US, Z codes are rarely used. This lack of uptake is linked to the reluctant adoption of Z codes by health care providers (True 2021). It seems that, if a coded item isn’t funded, it isn’t used – understandably! At the same time, the American Medical Association, UnitedHealthCare, and the American Hospital Association have petitioned to expand Z codes to capture a broader range of SDH and with greater specificity (Jacobs 2021: 3206), suggesting a brighter future for Z codes.

Below I have produced two sample lists (1 & 2) to indicate the range and nature of specific Z codes. Sample list 1 points to the ways in which some Z codes appear to target “structural determinants” of health. The items on this list indicate support for a critical SDH that emphasises the role governments play in creating specific social, economic and environmental contexts that help to make people either “ill” or “well” (Bacchi 2009: 135). 

Sample list 2 contains items where the target for change is the individual (or their parents) rather than living and working conditions. By contrast with Sample list 1, it identifies social factors that work through individual behaviours, commonly referred to as “risk factors”. This variability in Z codes signals the diversity of positions within SDH, identified by Yadavendu (2013) as a shift from “holism” to “individualism”:

“The older, philosophical approach in public health involved a holistic, population-based understanding that emphasized historicity and interrelatedness to study health and disease in their larger socio-economic and political moorings. A newer tradition, which developed in the late 19th century following the acceptance of the germ theory in medicine, created positivist transitions in epidemiology. In the form of risk factors, a reductionist model of health and disease became pervasive in clinical and molecular epidemiology.”

Z codes: Sample list 1 

Z55.1 Schooling unavailable and unattainable

Z56.3 Stressful work schedule

Z56.81 Sexual harassment on the job

Z57.0 Occupational exposure to noise

Z58.6 Inadequate drinking-water supply

Z59.1 Inadequate housing

Z60.5 Social exclusion and rejection

 

Z codes: Sample list 2

Z55.3 Underachievement in school 

Z59.2 Discord with neighbors, lodgers and landlord

Z60.0 Problems of adjustment to life-cycle transitions

Z62.1 Parental overprotection

Z62.891 Sibling rivalry

Z63.5 Disruption of family by separation and divorce

Z72.3 Lack of physical exercise 

Z72.51 High risk heterosexual behavior

Z72.52 High risk homosexual behavior

To deal with this plurality of perspectives, I suggest treating each Z-coded item as a proposal, in the WPR sense of the term, and submitting it to a WPR analysis ( KEYNOTE ADDRESS – CAROL BACCHI – 17 August 2022). Collectively, the sample lists also signal the need for concern about the accumulation of highly personal information for the purposes of governmental administration.

Finally, to repeat a theme running through this entry, to the extent that Z codes aim to offer causes of disease entities, they remain within what Armstrong (2021) calls “the pathological explanation”. The focus remains on disease entities rather than on the conditions a person lives in and with – the social and environment conditions that shape their lives, not just their deaths. The task becomes shifting from a conception of health as absence of disease to health as a “state of complete physical, mental and social wellbeing” (WHO 1946). 

Returning to the pandemic

I opened this entry by highlighting the extensive research attesting to the impact of socioeconomic conditions on COVID-19 death rates and queried the policy interventions that might usefully follow. Here I consider the extent to which a governmental response adopted in many countries, testing and contract tracing, pays heed (or not) to SDH.  

I draw on the work of Richard et al. (2023) who set out to evaluate the space for a focus on SIH (social inequalities in health; a proxy for SDH) in the design of testing and contact tracing (CoviTCT) protocols in France. Health inequalities, such as differences in life expectancy by social groupings, are seen to be the outcome of SIH (Marmot 2005).

Richard et al. (2023: 8) explicitly invite readers to approach the testing and contract tracing response as a problematization. They point out that, as a proposal, CoviTCT centres on the virus and contaminants. The primary goal is to break chains of transmission. 

Such a representation of the problem, they argue, participates “in (re)producing a predominant clinical framing (Benford and Snow, 2000) of the pandemic situation” (Richard et al. 2023: 8; see Townley et al., 2023). 

While the “key informants” interviewed by Richard et al. (2023: 5) adopted positions on a continuum ranging from a biomedical to a health promotion vision, a SIH perspective emerged only as a complement to the clinical epidemiological strategy of the CoviTCT intervention.  As a political response Richard et al. (2023: 9) stress the need to elevate the importance of SIH reduction approaches:

“From a dual perspective of social justice and epidemiological coherence, the COVID-19 pandemic has highlighted that SIH need to be considered as the basis of infectious diseases response, regardless of the emergency context.” 

In terms of effects (WPR question 5), interventions that aim to control the transmission of the virus tend to produce a focus on what Waleed Aly (2020) calls the “symptoms” of COVID-19. We are encouraged to see “technologies” such as CoviTCT as “solving” (albeit in a piecemeal fashion) the “problem” of contagion. There is no space in this reactive approach to consider how we have got here – how pandemics, for example, are an increasing likelihood due to a range of practices such as deforestation (Zimmer 2011). 

None of these comments suggests that we do not need testing and contact tracing. The point is to see how such a proposal/intervention creates the “problem”, what it assumes and what it leaves out. This approach could profitably be applied to other public health interventions, such as social distancing, mask wearing, working from home and “hard” lockdown (Kelaita et al. 2023). 

Tasks for another day

To argue, as I have done, that death certificates need redesigning to acknowledge explicitly the place of SDH does not on its own indicate what ought to be included on, or dropped from, the form. Rather, it highlights the need to encourage conversations on the design, assumptions and effects of this governmental mechanism. On the one hand there is a need to reflect on the limitations of a death certification process that “forces the use of a chain of linear, single diagnoses to describe the complex process by which people live and die” (Merlin et al. 2022: S37). On the other hand, there is an opportunity to engage with those who argue that this decontextualizing of death may be “an inevitable way of getting things done in medicine” (Timmermans and Almeling 2009: 23). 

In the short term, it is interesting to consider what might follow from encouraging doctors to use Z code categories carefully when they fill in death certificates. I say carefully, of course, due to the possibility of lapsing into individual behavioural categories of analysis and the possibility of extracting personal information that has no business being included on administrative forms of any kind (discussion above). Navigating the edges of the cleft stick I mentioned earlier is doubtless a challenging task for all of us.

I would be keen to hear reactions to the last three Research Hub entries on COD (Cause of Death). Have they managed to pique your interest in the political implications of standard administrative forms such as death certificates? Please contact me at: carol.bacchi@adelaide.edu.au or subscribe to the WPR list (see this link:  Welcome to the WPR Network! | Karlstad University (kau.se)

In the next entry I intend to deliver on my promise to examine what happens when WPR is applied to WPR. 

References

Adeyinka, A. and Bailey, K. 2022. Death Certification. National Library of Medicine, 21 April. Available at: https://www.ncbi.nlm.nih.gov/books/NBK526015/

AIHW (Australian Institute of Health and Welfare) 2012. Multiple causes of death in Australia: an analysis of all natural and selected chronic disease causes of death 1997-2007. Available at: https://www.aihw.gov.au/reports/life-expectancy-death/multiple-causes-of-death-in-australia-an-analysis/contents/introduction

AIHW (Australian Institute of Health and Welfare) 2022. Deaths in Australia. AIHW, Australian Government. Available at: https://www.aihw.gov.au/reports/life-expectancy-death/deaths-in-australia/contents/about Accessed 12 January 2023. 

Aly, W. 2020. Get to the root of mess. The Melbourne Age, Saturday 26 Dec., p. 48.

Armstrong, D. 2021. The COVID-19 pandemic and cause of death. Sociology of Health & Illness, DOI: 10.1111/1467-9566.13347 

Australian Bureau of Statistics (ABS) 2020. Causes of Death, Australia: Doctor Certified Deaths, Summary Tables. Available at: https://www.abs.gov.au/statistics/health/causes-death/causes-death-australia-doctor-certified-deaths-summary-tables/latest-release

Bacchi, C. 2008. The politics of research management: Reflections on the gap between what we ‘know’ [about SDH] and what we do. Health Sociology Review, 17(2): 165-176. 

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

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

Bambra, C., Fox, D. and Scott-Samuel, A. 2005. Towards a politics of health. Health Promotion International, 20(2): 187-193.

Benford, R. D. and Snow, D. A. 2000. Framing processes and social movements: an overview and assessment. Annual Review of Sociology, 26, 611–639. 

Bowker, G. C. 1996. The History of Information Infrastructures: The Case of the International Classification of Diseases. Information Processing & Management, 32(1): 49-61.  

Brooks, E. G. and Reed, K. D. 2015. Principles and Pitfalls: a Guide to Death Certification. Clinical Medicine & Research, 13(2): 74-82.

Chávez-Almazán, L. A., Díaz-González, L., Rosales-Rivera, M. 2022. Socioeconomic determinants of health and COVID-19 in Mexico. Gac Med Mex,  158: 3–10.

Centers for Disease Control and Prevention (CDC) 2021. Social determinants of health Z codes. Available at: https://beonbrand.getbynder.com/m/54b696f73cb32f14/original/Social-Determinants-of-Health-Z-codes-flyer.pdf

Dalsania, A. K., Fastiggi, M. J., Kahlam, A., et al. 2022. The relationship between social determinants of health and racial disparities in COVID-19 mortality. J Racial Ethn Health Disparities,  9: 288–95.

Figueiredo, A.M., Figueiredo, DCMM, Gomes, L. B., et al. 2020. Social determinants of health and COVID-19 infection in Brazil:
an analysis of the pandemic. Rev Bras Enferm, 
73 (suppl 2): e20200673.

Foucault, M. 1991. Governmentality. In G. Burchill, C. Gordon, & P. Miller (Eds), The Foucault effect: Studies in governmentality. Chicago: University of Chicago. 

Glover, J., Harris, K., Tennant, S. and Watts, V. 1999. A Social Health Atlas of Australia. Series of nine atlases covering each State and Territory, and Australia. Public Health Information Development Unit, University of Adelaide, Adelaide.

Goldacre, M. J., Duncan, M. E., Griffith, M. and Cook-Mozaffari, P. 2006. Psychiatric disorders certified on death certificates in an English population. Soc Psychiatry Psychiatr Epidemiol, 41(5): 409-414. 

Harvey, P.W. 2006. Social determinants of health: Why we continue to ignore them in the search of improved population health outcomes! Australian Health Review, 30(4): 419-423.   

Jacobs, Z. G. 2021. Codifying Social Determinants of Health: a Gap in the ICD-10-CM. J Gen Intern Med, 36(10): 3205-3207. 

Jewson, N. D. 2009. The disappearance of the sick-man from medical cosmology, 1770-1870. International Journal of Epidemiology, 38: 622-633.

Kelaita, P., Pienaar, K.,  Keaney, J., Murphy, D., Vally, H. & Bennett, C. M. 2023. Pandemic policing and the construction of publics: an analysis of COVID-19 lockdowns in public housing. Health Sociology Review, DOI: 10.1080/14461242.2023.2170260 

Lakkireddy, D. R., Gowda, M. S., Murray, C.W., Basarakodu, K.R., Vacek, J.L. 2004. Death certificate completion: how well are physicians trained and are cardiovascular causes overstated? Am J Med, 117:492-498.

Lang, K. 2022. How are COVID-19 deaths counted, and what does this mean? Medical News Today. Available at: https://www.medicalnewstoday.com/articles/how-are-covid-19-deaths-counted-and-what-does-this-mean

Lewis, J. 2005. Health Policy and Politics: Networks, Ideas and Power. Melbourne: IP Communications.

Marmot, M. 2005. Social determinants of health inequalities. Lancet, 365(9464): 1099-104. doi: 10.1016/S0140-6736(05)71146-6.

McGinnis, M. and Forge, W. 1993. Actual Causes of Death in the United States. JAMA, Dec. DOI: 10.1001/jama.270.18.2207 · 

McGowan, V. J. and Bambra, C. 2022. COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities. Lancet Public Health, 7: e966-75.

Merlin, J. S., Wachterman, M. W., Becker, W. C. and Bohnert, A. S. B. 2022. Limitations of Death Certificate-Based Data: Urgent Lessons from the Opioid Epidemic. AJPH, Supplement 1, 112(51): S36-S38.

Morgan, A., Andrew, T., Guerra, S., Luna, V., Davies, L. and Rees, J. 2022. Provider reported challenges with completing death certificates: A focus group study demonstrating potential sources of error. PLoS ONE 17(5): e0268566. https://doi. org/10.1371/journal.pone.0268566 

Pagidipati, N. J., and Gaziano, T. A. 2013. Estimating Deaths From Cardiovascular Disease: A Review of Global Methodologies of Mortality Measurement. Circulation, 127: 749-756.

Raphael, D. 2006. Social determinants of health: Present status, unanswered questions, and future directions. International Journal of Health Services, 36(4): 651-677.

Richard, Z., Chabrol, F., Gautier, L., Zinszer, K. and Ridde, V. 2023. Considering social inequalities in health in COVID-19 response: insights from a French case study. Health Promotion International, 38: 1-11.

Rockett, I. R. H., Kapusta, N. D. and Bhandari, R. 2011. Suicide Misclassification in an International Context: Revisitation and Update. Suicidology Online, 2: 48-61.  

Samuel, L. J., Gaskin, D.J., Trujillo, A.J., Szanton, S.L., Samuel, A., Slade, E. 2021. Race, ethnicity, poverty and the social determinants of the coronavirus divide: U.S. county-level disparities and risk factors. BMC Public Health, 21: 1250.

Singu, S., Acharya, A., Challagudla, K. and Byrareddy, S. N. 2020. Impact of Social Determinants of Health on the Emerging COVID-19 Pandemic in the United States. Frontiers in Public Health, 21 July. doi: 10.3389/fpubh.2020.00406 

Stirton, F. D. and Heslop, P. 2018. Medical Certificates of Cause of Death for people with intellectual disabilities: A systematic literature review. J Appl Res Intellect Disabil. 31: 659–668.   

Swain, G. R., Ward, G. K., & Hartlaub, P. P. (2005). Death certificates: Let’s get it right. American Family Physician, 71(4),  652– 656.

Timmermans, S. and Almeling, R. 2009. Objectification, standardization, and commodification in health care: A conceptual readjustment. Social Science & Medicine, 69: 21-27.

Townley, C., Properjohn, C., Grace, R. & McClean, T. 2023. Stay home, stay safe? Public health assumptions about how we live with COVID, Health Sociology Review, DOI: 10.1080/14461242.2023.2199724  

True, J. 2021. Cracking the Code to Better Health. U.S. News. Available at: https://www.usnews.com/news/health-news/articles/2021-01-13/how-underused-medical-codes-for-social-needs-could-transform-health-care

Vigo, D., Thornicroft, G., & Atun, R. 2016. Estimating the true global burden of mental illness. The Lancet Psychiatry, 3(2): 171-178. https://doi.org/10.1016/S2215-0366(15)00505-2

World Health Organization 1946. Preamble to the Constitution, as adopted by the International Health Conference, 22 July. Available at: https://www.who.int/about/governance/constitution Accessed 9 Feb. 2023. 

World Health Organization 2009. Commission on the Social Determinants of Health. Report by the Secretariat. Available at: https://apps.who.int/iris/handle/10665/2189Accessed 9 Feb. 2023.

World Health Organization 2021. COVID-19 and the social determinants of health and health equity: evidence brief. Available at: https://apps.who.int/iris/handle/10665/348333

World Health Organization 2022a. WHO’s new International Classification of Diseases (ICD-11) comes into Effect. Available at: https://www.who.int/news/item/11-02-2022-who-s-new-international-classification-of-diseases-(icd-11)-comes-into-effect

World Health Organization 2022b. WHO ICD-11 Mortality Tooling. Available at: https://www.who.int/standards/classifications/classification-of-diseases/cause-of-death

World Health Organization 2023. The Social Determinants of Health. Available at:  https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1

Yadavendu, V. K. 2003. Changing Perspectives in Public Health: From Population to an Individual. Economic and Political Weekly, 38(49): 5180-5188.

Yadavendu, V. K. 2013. Shifting Paradigms in Public Health: From Holism to Individualism. NY: Springer.

Zhu, Y., Nam, S., Quan, L., Baek, J., Jeon, H., and Tang, B. 2022. Linking suicide and social determinants of health in South Korea: An investigation of structural determinants. Frontiers in Public Health, DOI 10.3389/fpubh.2022.1022790 Zimmer, C. 2011. A Planet of Viruses. University of Chicago Press.

In the recent elaboration of key premises in WPR ( KEYNOTE ADDRESS – CAROL BACCHI – 17 August 2022) I emphasize the importance of cultivating a genealogical sensibility. The argument supporting this proposition is that we need to consider how we have got “here” from “there” because such a focus makes it possible to question the present. Foucault (1990) calls this approach “a history of the present”. Importantly, a genealogical sensibility resists the temptation to offer a strict chronology of developments, which can lead to a sense of inevitability. For Foucault (1977), relying on Nietzsche, genealogies are records of discontinuity, of twists and turns, of skeletons in the closet (if you will). A genealogical sensibility therefore keeps a sharp eye open for tensions and debates around crucial issues. To those who find this detail dull, I apologize. My training as a historian means I love this stuff!

The history of the current standardized death certificate is long and complicated, and way beyond the purview of a Research Hub entry. Some of the research in this entry is original, but mostly I draw on secondary sources. I bring particular attention to contestatory frames of reference. As a uniting theme, I highlight debates about the countervailing influences of biology and environment on human development. To undertake this task, following Dean (1999: 178), I identify the forms of knowledge that make COD thinkable, the techniques that discover it, the technologies that seek to govern it and the political rationalities and programs that deploy it.  Keep your eye open for these knowledge and other practices, and for resonances with (not replicas of) contemporary developments!

Bills of Mortality, John Graunt and “Shop-Arithmetique”

Bills of Mortality were produced in late 16^th century London, primarily to monitor cases of the plague and to institute quarantine where needed. Parish clerks kept the statistics. Causes of death other than plague were recorded from about 1604 (Connor 2022: 2). 

A London draper, John Graunt (1620-74), produced a systematic study of the Bills of Mortality in 1662. He applied what he described as “the Mathematiques of my Shop-Arithmetique” – his book-keeping skills – to the data. His tables indicate sharp differences in annual death rates between what Graunt called “Acute” or “Epidemical” diseases and those which he described as “Chronical”, a distinction that can be traced to the time of Hippocrates. 

Graunt drew attention to the poor health and reduced longevity of those living in large geographical regions, a phenomenon that became known as the “Urban Penalty”. In his words, 

I considered, whether a City, as it becomes more populous, doth not, for that very cause, become more unhealthfull, I inclined to believe, that London now is more unhealthfull, then heretofore, partly for that it is more populous, but chiefly, because I have heard, that 60 years ago few Sea-Coals were burnt in London, which now are universally used. For I have heard, that Newcastle is more unhealthfull than other places, and that many People cannot at all endure the smoak of London, not onely for its unpleasantness, but for the suffocations which it causes.  (Graunt 1662 in Connor 2022: 5)

(I feel tempted to say: “plus ça change, plus c’est la même chose”.)

Graunt tied the importance of demographic and epidemiological data to the need to keep Government and Trade “certain and regular”, with consequent benefits for peace and prosperity (Connor 2022: 6). He expressed misgivings about the effectiveness of quarantine against contagion: “That the troublesome seclusions in the Plague-time is not a remedy to be purchased at vast inconveniences” (Connor 2022: 6), a view that may sound familiar. 

Graunt’s friend and colleague, William Petty, coined the term “Political Arithmetic” to describe the connection between demography and government. Defined by Charles Davenant (1656-1714) as “the art of reasoning by figures, upon things relating to government”, this thinking is considered the forerunner of economics as a discipline (Hoppit 1996 in Connor 2022).

The Power of Statistics

The other form of knowledge implicated in the evolving enthusiasm for COD accounting is statistics. Moriyama (2011: 10) traces the “triggering event leading to the first ICD [International Classification of Diseases]” to “the unlikely Great Exhibition of 1851 held at the Crystal Palace in London”. At that event numerous nations displayed their industrial products. Visiting statisticians pursued the idea of comparing national statistics, which led to the First International Statistical Congress at Brussels in 1853. Moriyama reports: “By this time, a systematic review seems to have occurred of subjects that could be candidates for international statistical comparison”, including “Causes of Death”. 

Following a proposal at the 1853 Congress to produce a uniform nomenclature of diseases, which was needed to tabulate “causes of death”, William Farr and Jacob Marc D’Espine were charged with developing classifications. Farr (1807-1883) was a physician employed as a British government statistician (Alharbi et al. 2021: 4). D’Espine was a Swiss statistician. D’Espine’s list grouped causes according to their nature, that is, as “gouty, herpetic, hematic”, etc. Farr divided diseases into three classes:

the first for those that occur endemically or epidemically, in other words, the communicable diseases, which provided an index of salubrity [health or well-being]. The second class was for those diseases that arise sporadically—these he subdivided anatomically into diseases of the nervous system, respiratory organs, etc., ending with a group for those of uncertain location such as tumors, malformations, debility, sudden death, and old age.  (Moriyama 2011: 10)

According to the president of the committee overseeing the project, classification of groups of the diseases had only secondary importance: “the main point was to produce a list of morbid entities frequent enough to merit the attention of the statistician, enabling comparison of data on known morbid entities” (Moriyama 2011: 11; emphasis added). In the event, Farr’s list, which included the principle of classifying diseases by etiology followed by anatomic site, survives in the present classification. As Armstong (2021: 1621) points out, the nineteenth century decision to separate epidemic, sporadic/pathological and violence as causes of death has continued to underpin the classification of death into the 21^st century.

In this classification model, epidemics supersede deaths from pathological causes as “underlying cause of death”. At the First Revision Conference on the International List of Causes of Death in 1890, Jacques Bertillon, Chief of Statistics for the City of Paris, prepared a guide for medical officers responsible for determining COD. His third rule stipulated: 

When among the two causes of death there is a transmittable disease, it is preferable to assign the death to it, for statistics of infectious diseases are particularly interesting to the sanitarian [whom we shall meet shortly CB], and it is important that they shall be as complete as possible. (in Moriyama 2011: 30)

This privileging of epidemic disease as underlying cause of death continued into the twentieth century. In 1958 Carl Erhardt, Director of the Bureau of Records and Statistics, Department of Health, New York, endorsed this thinking: “In populations with high mortality and short life expectancy, death is most often caused by infectious or communicable disease. Under such circumstances, the cause of death can often be expressed as a single term” (Erhardt 1958: 163). This convention explains the WHO (2020: 3) directive to doctors and coroners to list COVID-19 as the “underlying cause of death” (see previous entry 30 May 2023).

Sanitation and COD

The mid-19^th century proved to be a pivotal time for discussions around COD. In 1837 the Registration Act was passed in England. It included provisions for inquiry into causes of death. In 1839 William Farr, whom we have already met, became compiler of abstracts in the Registrar-General’s office. He developed and analyzed mortality statistics to do with the conditions of sanitation and health in England (Moriyama 2011: 2). Farr drew direct links between environmental factors and health/death. Registration districts were characterized by “degrees of insalubrity”, depending on whether they exceeded the established standard of 17/1000 deaths. Mortality in excess of this rate was “assumed to be due to defects in the environment” (Eyler 1987: 343). 

Farr’s 1837 mortality report included a comment on 63 deaths resulting from “starvation”: “Hunger destroys a much higher proportion than is indicated by the registers in this and every country, but its effects, like the effects of excess, are generally manifested in the production of diseases of various kinds” (in Whitehead 2000: 87). “Natural death”, in his account, could be accelerated by “want and privations” (in Armstrong 2021: 1621). 

Eyler (1976) notes that health reformers and sanitarians accepted a view that health, disease, death, and physical and moral conditions were joined in an indissoluble link. In Farr’s (1875 in Eyler 1976: 336) explanation:

There is a relation betwixt death and sickness. … There is a relation betwixt death, health, and energy of body and mind. There is a relation betwixt death, birth, and marriage. There is a relation betwixt death and national primacy. … There is a relation betwixt the forms of death and moral excellence or infamy. 

The Medical Officer to the Privy Council, John Simon (1857-58 in Eyler 1975: 336), added to Farr’s list a supposed relation between infant mortality and the causes leading to racial degeneration. In this ordered world where everything linked up so neatly, statistics promised to provide an effective tool to “elucidate the complex dependency of health on manner of life”. A driving force in this discussion was Florence Nightingale, who aimed to demonstrate statistically how improved sanitary conditions and better schooling reduced mortality, illness, and even criminal behaviour (Moriyama 2011: 11).

There were tensions among the sanitation campaigners. The well-known first secretary to the Poor Law Board and later Commissioner of the General Board of Health, Edwin Chadwick, objected to Farr’s inclusion of the category “starvation” in the Bills of Mortality. Chadwick argued that it was impossible for a person to starve to death in London. “The notion that poverty itself was the cause of illness was, for Chadwick, unthinkable” (Corbett 1999). Chadwick had served as private secretary to Jeremy Bentham, the utilitarian philosopher. The Benthamite prescription for pauperism was deterrence: “The pain of the workhouse was to be greater than the pain of poverty and poor relief” (Corbett 1999).

It is too simple to paint Farr as a proponent of the social determinants of health and Chadwick as the defender of the evil workhouse. As Armstrong (2021: 1621) points out, Farr’s focus on “external agents”, such as “intemperance, cold, want, and effluvial poisons”, did not disrupt the dominant biomedical or pathological explanation of death with its focus on “disease entities”. All that Farr accomplished, and it is no mean accomplishment, was to highlight the range of influences in people’s environments that cause disease. This theme – whether it is possible to disrupt “the pathological explanation” in death certificates – is taken up in the next Research Hub entry.

In addition, it is useful to consider how Farr’s statistical frame of reference placed limitations on his social analysis.  He wanted to make statistical inferences and believed this could not be done with the small numbers that would be produced in a detailed classification. Hence, he did not provide specific rubrics for diseases that were rare in England (Moriyama 2011: 10), imposing an Anglo-Celtic analytic lens.

Challenges to “salubrity” 

The opposition to our sanitarians, which developed later in the century, came from several quarters. On one side were the anti-vaxxers (yes, indeed). The Reverend and Mrs William Hume-Rothery led the National Anti-Compulsory Vaccination League. Their targets were the Compulsory Vaccination Acts of 1867, 1871 and 1874 (we are talking about smallpox here). Mrs Hume-Rothery questioned male domination of medicine, especially male tyranny over female patients. She strongly objected to the proposal to give qualified practitioners the sole right of signing death registers. State medicine, she claimed, threatened personal liberty and morality: “It seemed a conspiracy of a greedy profession against the public”. Her National Anti-Compulsory-Vaccination Reporter carried the following articles: 

“The Swedish Small-Pox Statistics Fraud: The Real Truth of the Matter,” 1882, 5:25-29 

“How Pro-Vaccinators Manipulate Statistics,” 1883, 7:195-97, 214-16

 (see Eyler 1976: 347-348)

Other opponents of the sanitary reformers, clearly with very different arguments, were local medical authorities. They voiced no objections to the role of physicians in public policy but, based on their experience, they expressed doubts that death rates were a good test of the healthiness of their respective districts. According to Eyler (1976: 348), this opposition signalled “the growing medical domination of the public health movement”. The president of the Society of Medical Officers of Health asked the Statistical Society of London in 1874 if death rates could be lowered with sanitary reform: “Would it do away with the overcrowding that was one of the necessary conditions of the existence of people in large towns?” (Eyler 1976: 348).

The chief challenge to mortality rates as measures of community health was that “Implicit in the program of these social pathologists is the belief that length of life reveals quality of life” (Eyler 1976: 354). In reaction, a demand for compulsory registration of sickness became common, led by the International Statistical Society, the Epidemiological Society, and a joint committee of the Social Science and the British Medical association (Eyler 1976: 354). It was 1911 before national compulsory registration of contagious diseases was enacted. 

“National primacy”, COD and eugenics

The identified relation Farr makes “betwixt death and national primacy” (see above), and Simon’s reference to “racial degeneration”, noted earlier, suggest links between the development of COD accounting and the eugenics movement. In previous work – actually in my previous life as a historian – I explored the complex variations in Australian eugenic thought between 1900 and 1914 (Bacchi 1980). Of some interest to the topic at hand is the distinction I identified between so-called positive eugenicists and negative eugenicists. The former put their faith for improving the “race” (and they did speak in these terms) in environmental change and the latter in biological interventions, including compulsory sterilization of the so-called “feeble minded”. At first glance, there appears to be a clear dispute between those who promoted environmental and those who offered biological explanations for human development. However, the two groups shared a belief in the prominent place of biology but, according to our “positives”, there remained some room to move. While the priority placed on national/racial improvement needs to be recognized, it is important to keep an eye open to divergence and contradiction, and just plain messiness, “to show that things ‘weren’t as necessary as all that’” (Foucault 1991: 76). 

As an example, Professor Raymond Pearl (1879-1940) from Johns Hopkins University, was an avid supporter of the eugenics movement in the early 1900s. But in 1925 he wrote a book condemning the movement as unscientific. Professor of the Blomberg School of Public Health, he was a founder of biometry, the application of statistics to biology and medicine. He used his statistical studies to demonstrate the connection between smoking and death, and to trace the varying times of death for body organs, useful in current transplantation. He was highly critical of the International Classification of Causes of Death, describing it as “not primarily a biological classification”. He proposed modifying the list to group all causes of death under the heads of the several organ systems of the body:

We are now looking at the question of death from the standpoint of the pure biologist, who concerns himself not with what causes a cessation of function, but rather with what part of the organism ceases to function, and therefore causes death. (Pearl 1921: 491) 

As a “pure biologist”, Pearl endorsed a view of the body as machine, though he was far from impressed by the nature of evolution: “The workmanship of evolution, from a mechanical point of view, is extraordinarily like that of the average automobile repair man” (Pearl 1921: 516). Hence, he questioned some of the strategies of the eugenicists. Since a brown-eyed man and a brown-eyed woman might have blue-eyed children, he could see no “guarantee that a wealthy, intelligent, tall, and handsome couple would not have children who grew up to be poor, stupid, short, and ugly” (Hendricks 2006). However, while on occasion he questioned the class bias of eugenics, he continued to express racist and anti-Semitic views.

Pearl supported birth control as a public health measure, targeting what eugenicists referred to as the “differential birth rate” (Bacchi 1980: 208). His expressed goal was to reduce the high birth rate in many non-industrialized countries and in lower income groups within the industrialized world, indicating a continuing class-based prejudice. He held out hope that child welfare might provide a useful direction to reduce the general death rate, with his focus primarily on education:

Ignorant and stupid people must be taught, gently if possible, forcibly if necessary, how to take care of a baby, both before and after it is born. It seems at present unlikely, that mundane law will regard feeding cucumber to a two months’ old baby, or dispensing milk reeking with deadly poison makers, as activities accessory to first-degree murder. (Pearl 1921: 494). 

At the same time Pearl endorsed public hygiene initiatives:

… if that final Judgment Seat, before which so many believe we must all eventually appear, dispenses the even-handed justice which in decency it must, many of our most prominent citizens who in the financial interests of themselves or their class block every move towards better sewage disposal, water and milk supply, and the like, or force pregnant women to slave over a washtub and sewing bench that they may live, will find themselves irrevocably indicted for the wanton and willful slaughter of innocent babies. (Pearl 1921: 495). 

With all this, he finally concludes that biology trumps environment: 

It furthermore seems to me that the results presented in this paper add one more link to the already strong chain of evidence which indicates the highly important part played by innate constitutional biological factors as contrasted with environmental factors in the determination of the observed rates of human mortality. (Pearl 1921: 516)

I have offered Pearl, not as a confused and bombastic biologist, but to illustrate that, when it comes to causes of death, the picture is complicated. At the same time Pearl’s work illustrates the primary focus in death certificates on biology in one form or another – a focus either on disease entities, or on organ systems. Efforts to bring factors from outside that framework to bear on conceptions of death causation (COD) fight an up-hill battle. In the last entry in the series (next entry), I concentrate on interventions designed to bring awareness of the social determinants of health to the discussion of COD. 

References

Alharbi, M. A., Isoudard, G. and Tolchard, B. 2021. Historical development of the statistical classification of causes of death and disease. Cogent Medicine, 8:1, 1893422, DOI: 10.1080/2331205X.2021.1893422 

Armstrong, D. 2021. The COVID-19 pandemic and cause of death. Sociology of Health & Illness, DOI: 10.1111/1467-9566.13347

Bacchi, C. 1980. The nature-nurture debate in Australia, 1900-1914. Historical Studies, 19(75): 199-202. 

Bowker, G. C. 1996. The History of Information Infrastructures: The Case of the International Classification of Diseases. Information Processing & Management, 32(1): 49-61.  

Connor, H. 2022. John Graunt F. R. S. (1620-74): The founding father of human demography, epidemiology and vital statistics. Journal of Medical Biography, DOI: 10.1177/09677720221079826. 

Corbett, S. 1999. Review: C. Hamlin, Public Health and Social Justice in the Age of Chadwick Britain 1800-1854.  Health Promotion International, 14(4): 381-382. 

Dean, M. (1999). Governmentality: Power and rule in modern society. London: Sage.

Erhardt, C. L. 1958. What is “The Cause of Death”?  Journal of the American Medical Association (JAMA), 168(2): 161-168.

Eyler, J. M. 1976. Mortality Statistics and Victorian Health Policy: Program and Criticism. Bulletin of the History of Medicine, 50(3): 335-355. 

Farr, W. 1875. Letter to The Registrar-General on the Mortality in the Registration Districts of England during the Years 1861-70, Supplement to the Thirty-fifth Annual Report of the Registrar-General of Births, Deaths, and Marriages in England, 1875 [C. 1 155-1]. XVIII, pt. 2] 

Foucault, M. 1977. Nietzsche, genealogy, history. In D.F. Bouchard, (Ed.), Language, counter-memory, practice: Selected essays and interviews. 

Ithaca: Cornell University Press.  

Foucault, M. 1990. The concern for truth. In L.D. Kritzman (Ed.), Michel Foucault: Politics, philosophy, culture. Interviews and other writings, 1977–1984, 1st edition 1988, A. Sheridan, (trans.). New York: Routledge.

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

Graunt, J. Natural and political observations mentioned in a following index, and made upon the bills of mortality. London: John Martin, 1662.

Hendricks, M. 2006.  Raymond Pearl’s “Mingled Mess”, Johns Hopkins Magazine. Available at: https://pages.jh.edu/jhumag/0406web/pearl.html

Hoppit J. 1996. Political arithmetic in eighteenth-century England. The Economic History Review New Series, 49: 516–540.

Moriyama, I. M. 2011. History of the Statistical Classification of Diseases and Causes of Death. Centers for Disease Control and Prevention, National Center for Health Statistics.

Pearl, R. 1921. The Biology of Death – IV. The Causes of Death. The Scientific Monthly, June. 

Simon, J. 1857-58. Introductory Report by the Medical Officer of the [General] Board of [of Health], Papers Relating to the Sanitary State of the People of England, [2415]. XXIII, xxvii. 

Whitehead, M. 2000. William Farr’s legacy to the study 

of inequalities in health. Bulletin of the World Health 

Organization, 78(1): 86–87.

World Health Organization (WHO) 2020. International guidelines for certification and classification (coding) of COVID–19 as cause of death. https://www.who.int/publications/m/item/international-guidelines-for-certification-and-classification-%28coding%29-of-covid-19-as-cause-of-death