Data Politics and Indigenous Representation in Australian Statistics

Introduction

The chapter addresses the complexities of applying numerical data to Indigenous populations in colonized countries like Australia, New Zealand, Canada, and the United States.

The core argument is that statistics are not neutral but are imbued with the dominant social norms, values, and racial hierarchies of the societies in which they are created. This introduces a 'raced reality' in statistics applied to Indigenous peoples.

The chapter aims to investigate how Australia's racial terrain influences statistics on Indigenous Australians, examining both the presence and absence of certain data. It also challenges researchers to consider how an Indigenous methodological framework could reshape the narrative and policy directions.

Five-D Data and the Statistical Indigene

The chapter introduces the concept of '5D data', which summarizes the common statistical representations of Indigenous Australians as disparity, deprivation, disadvantage, dysfunction, and difference. Examples include data from the Australian Human Rights Commission, the Australian Bureau of Statistics, and the Australian Institute of Health and Welfare, which often highlight socioeconomic and health inequities.

The author notes that data on Aboriginal social phenomena unrelated to the 'five Ds' are scarce. Cites Ting et al. (2015), whose research on the division of household labor found more egalitarianism in Indigenous households but was limited by sample size.

The scarcity of data outside the 5D framework reveals a problematic positionality of Aboriginal and Torres Strait Islander people within the statistical landscape of Australia.

The collection of data on socioeconomic and demographic disparities is necessary, but there is a lack of other data considered critical for the majority population. The question is raised as to why large-scale studies like the HILDA survey did not prioritize a sufficient Indigenous sample and why understanding Aboriginal peoples outside the lens of social problems is often overlooked.

5D Data and the Deficit Data/Problematic People Correlation

The chapter discusses the 'deficit data/problematic people (DD/PP) correlation', arguing that societal inequality is often correlated with racial unfitness. This concept is not unique to Australia, as Tuhiwai Smith (1999) argues that numbers rationalize dispossession and marginalization.

The DD/PP correlation suggests that inequality is a result of the behavior and choices of Indigenous people, the relationship is disagreed with by researchers. This correlation is used to rationalize policies such as community closures, linking them to violence and suicide.

The concept aligns with theories of new racism, where racial biological inferiority is replaced with cultural and moral inferiority as explanations for socioeconomic disparity, as argued by researchers like Bobo (1997), Kinder and Sears (1981), and Bonilla-Silva (2010).

Bonilla-Silva (2010) contends that claims of non-white inferiority can be made alongside claims of non-racism, a concept termed 'racism without racists'.

Colonization is a key factor framing racial/social hierarchies in Australia, supported by racialized discourses that define Indigenous peoples based on 5D data depictions.

The 'politics of data' has significant consequences for the relationship between the nation-state and Indigenous populations. Stereotype-enhancing data pictures of Aboriginal deficits provide a circular rationale for inequality, overshadowing other explanations and veiling the misuse of state power.

Academic research is not immune to the DD/PP correlation, which in turn adds scholarly legitimation to the picture of Indigenous people as unfit and blameworthy. Weatherburn’s (2014) analysis of arrest, incarceration, socioeconomic and other statistics relating to Australian Indigenous people, concludes widespread criminality among Australian Indigenous peoples is to blame for the heavy overrepresentation of Indigenous people in incarceration. The growth in this overrepresentation, he argues, can be explained by the change in the relative rates of Indigenous and non-Indigenous involvement in serious crime. This analysis, however, makes the classic ‘correlation equals causation’ error.

How 5D Data Construct the Dominant Discourse on Indigeneity

The numerical format of statistics contributes to the normalization of the DD/PP correlation. Social and cultural phenomena are converted to numerical values, lending a sense of impartiality, if not full objectivity. Indigenous socio-structural realities are transmuted into neutral data points.

These numbers, positioned as objective descriptors, operate as mechanisms of unequal power relations, defining who and what Indigenous people are and limiting what they cannot be. Constant measurement and comparison of Indigenous positioning, to the exclusion of other investigations, reinforce these 5D portrayals. The advent of big data exacerbates the pejorative power of numbers to further marginalize and dispossess. It defines Indigenous people through the straitjacketing lens of deficit, with little chance to be viewed in any other positive light.

When the Only Aborigine You Know is the 5D Statistical Aborigine

The DD/PP correlation's impact is worsened by the separation between black and white lives. Indigenous lives are often out of sight and mind, except as pejorative stereotypes. This lack of knowledge about Indigenous people fosters the building of relations around pejorative stereotypes, casual disrespect, and denigration.

The author, as an Aboriginal person with pale skin, witnesses this discourse frequently, even in academic settings. Ideas of Aboriginal responsibility for their socioeconomic position and over-entitlement persist, despite evidence to the contrary.

The absence of Aboriginal and Torres Strait Islander people within the life orbits of non-Indigenous Australia supports discourses of disregard. 5D data allow the non-Indigenous majority population to be assured in their knowledge of Aboriginal and Torres Strait Islander people regardless of the fact that they are unlikely to know any Aboriginal or Torres Strait Islander people.

The chapter cites results from the 2007 Australian Survey of Social Attitudes (AuSSA), revealing that a modest majority of non-Indigenous Australians recognize contemporary racial inequality but do not necessarily support remedial action. This can be explained by the prevalence of 5D data and the associated DD/PP correlation.

Regression analysis of AuSSA data indicates that social proximity (interaction with Aboriginal people) is not significantly associated with attitudes toward Aboriginal issues, suggesting that dominant public discourses, including 5D data, are major informers of non-Indigenous attitudes.

$Table \textbf{5.1} OLS regression variable description and coefficients predicting ‘attitudes to Aboriginal issues’ scores$

Disrupting the Paradigm of Indigenous Statistics

The chapter revisits the questions of the reality, deployment, portrayal, and service of Indigenous statistical data. It argues that the numbers are deployed in limited ways, primarily portraying Indigenous deficit and reinforcing the subordinate Indigene position.

Disrupting this paradigm requires disturbing the established tropes of data on Indigenous people, both ontologically and epistemologically. The primary challenge is that Indigenous worldviews are not shaping the data.

Drawing on Bourdieu's concepts of social space and habitus, the chapter argues that the world view of those who control Indigenous data commissioning, analysis, and interpretation shapes how Indigenous statistics are understood and 'done'.

The chapter is also drawing on cultural theorists Hofstede and Hofstede (2005) argument that similar groups of people are mentally programmed with ‘software of the mind’ to produce similar constructs, which they form into logical, affective and behavioural models, this similar habitus of the primary creators of data on Indigenous Australians and their lifelong positioning as Euro-Australian middle-class people shape (subconsciously mostly) the production of data on Indigenous Australians and their subsequent portrayal, thereby confining and/or prescribing how these data are ‘done’.

Dominant discourses, not statistical methods, determine social data meanings. Claims of objective methodology allow dominant settler society questions to be perceived as the only questions. The power and politics of the data are embedded in who has the power to make assumptive determinations.

Research constructed from statistics and data imagined from Indigenous ways of seeing the world will change the terrain of Indigenous statistics. The Indigenous position in four-dimensional social space makes apparent the gaps in current frameworks and existing categories, concepts, and conceptualizations of Indigenous data.

Altering the paradigm of statistics on Indigenous people is critical if the statistical 'recognition gap' is to be addressed. Expanding the 'recognition space' allows for reframing narratives about Indigenous peoples in a language of statistical evidence that both Indigenous and non-Indigenous communities understand and respect.

A Case Study

The chapter presents a research example, 'Telling it Like it Is,' conducted in partnership with Larrakia Nation in Darwin, Australia, which aimed to redress the gap in Aboriginal views on Australian values and society. $('Telling it Like it Is’ is an Australian Research Council (ARC) funded research project conducted by Habibis, Walter and Elder. ARC Linkage Project 130100622. See also Habibis et al. (2016).)$

Initial results from interviews revealed a disconnect between Aboriginal and non-Aboriginal lives, with social interaction being more transactional than relational. Value disconnects centered on the contrast between Western material success and Aboriginal obligations to family and culture.

Survey data of over 400 Aboriginal people in Darwin confirmed the qualitative findings and revealed a lack of trust of Euro-Australian-dominated institutions and resentment of their refusal to recognize Aboriginal sovereignty. Negative racialized encounters with non-Indigenous residents remained a normalized experience.

Conclusion

Alternative-paradigm Indigenous statistics disrupt the status quo of Indigenous data production and the DD/PP correlation. This may disturb the ontological and epistemic security of those accustomed to the current data creation process.

Indigenous-framed numbers are powerful and political, reversing the one-way track of how Australia's racial terrain permeates Indigenous statistics.