Gaps in research kill people. This is the position People of Colour often find ourselves in. But this problem is made worse for People of Colour with disabilities.
The Canary has already looked at the lack of data available in this area. Research allows groups of people to be able to access structural support and to find networks of support. This time, we’ll be looking at the practical reasons why disabled People of Colour are missed out in data analysis – and what can be done about this.
Barriers to research
There are some practical problems here, before we get to the structural problems.
A lot of research in this area tends to be quantitative – analysis that uses numbers.
The Canary spoke to Myer Glickman, a senior statistician at the Office of National Statistics (ONS):
There are lots of practical barriers to aggregating data together. … One of the barriers is that we need numbers for analysis and the census gives us numbers. For example, let’s say in the whole country there are 8 million people in ethnic, but there’s probably less than 1 million of those who are disabled. So when we do a survey, and that may only include the information of 10,000 people, and from that you have 200 disabled people of colour. You can produce less statistical information about that group, and that is one of the big issues: losing the statistical ability to produce data.
A small sample size is a problem for quantitative researchers but Glickman agreed that disability and ethnicity deserves attention:
We’re about to have another census in spring 2021 that will bring in a new load of data and lots of analysis. Intersectionality is one of the things on our agenda, and we agree that disability and ethnicity is an under-served area of research.
Clearly, other research tactics are needed to address the issue of small sample size. People at the intersection of disability and race shouldn’t be left behind. Indeed, there is an argument to be made that policy that comes from race studies and policy that comes from disability studies can be significantly strengthened when the two areas are looked at together.
As The Canary has said previously in the #OurLivesOurStories series, these two areas are not experienced separately or one at once. Research should reflect that.
Glickman went on to clarify that:
We’re using qualitative research alongside quantitative analysis so we can highlight individual voices. That’s not traditionally been a strong area for the ONS but when dealing with smaller demographics and areas of particular lived experience, qualitative research helps add valuable research. … For example, not all disabled people experience disability in the same way. There are important differences in experiences and we are gradually extending what we do. When we have the new census we’ll be producing a lot more along these lines.
Qualitative research can be anything from interviews, focus groups, to questionnaires. This kind of research would allow a small sample size that shows the nuances of lived experiences for disabled People of Colour. Incoming data from the 2021 census will be vital for analysis that can better address the specific concerns of smaller groups of people. Good research practice needs to address who is missed out and qualitative research can help address this.
Lack of comment is comment
However, there needs to be commitment and desire to research racial minorities. Sadly, it’s simply not true that UK institutions are committed to anti-racist practice. When The Canary reached out to the Department of Health and Social Care, we explained this series on disability and asked if there were any policy decisions coming from the government that would look at the experiences of disabled People of Colour in some way.
The reply we received directed us to a parliamentary question about inequalities relating to the impact of coronavirus.
In this reply, Conservative life peer baroness Elizabeth Berridge promised that more research and data was incoming on how inequalities “including ethnicity, gender and obesity – can impact on people’s health outcomes from COVID-19”.
This is obviously necessary given the higher proportion of People of Colour dying from coronavirus. But we also didn’t ask that question.
Long Covid will undoubtedly have an impact on rising numbers of people with disabilities in the UK. However, if more People of Colour are disproportionately contracting coronavirus it makes sense to assume that long Covid will also disproportionately affect People of Colour. If there’s already a gap in research here, that gap will only widen with the impact of long Covid.
Berridge went on to say that the minister for equalities Kemi Badenoch has been asked by the prime minister to investigate these inequalities with respect to the ongoing pandemic.
That’s right, the same Kemi Badenoch who decried critical race theory.
How about some critical thinking skills
Badenoch has unwittingly struck on the underlying problem here. Critical race theory is a theory of sociology which examines race and structures of power. If critical race theory were better established in the UK then there would probably be a better understanding of the humanity and right to research on disabled People of Colour.
As is so painfully often the case with research and policy development on race, there has to be the political will to commit to research on Communities of Colour. In order for that to happen something basic needs to be in place: acknowledgements of systemic racism and the layers of impacts it has. That is not the case in the UK.
The practical problems (small sample size, a mixing of quantitative with qualitative) are held in place by the structural problem of racism.
Data analysis has a problem with racism
Sarah Chander, a senior policy advisor at European Digital Rights, told The Canary:
This conversation for me really links to the broader conversation on the inherent racialising and marginalising role of technology. In a world of deep, sustained inequalities, our default understanding must be that data, technologies and systems deigned to record, study and classify marginalised communities are flawed. They reflect the power imbalances of today and of history. In such a context, claims of neutrality are also political statements, ones which seek to derail from a more cohesive understanding of structures of oppression and historical imbalances.
As Audre Lorde famously said, “the master’s tools will never dismantle the master’s house“.
The tools of racism are purposely not equipped to break past systems of oppression. That is a choice. White supremacy is not just people running around in KKK hoods. It also manifests in complex and layered ways.
Not having a policy for an area or claiming to be neutral is a political statement. Chander goes on to point out that:
Little analysis is applied as to how such processes disproportionately impact racialised and other marginalised communities. Even less attention is paid to the historical roots of these methods of data collection. For example, many types of biometric data collection are innovations of the colonial project (such as fingerprinting, stemming from colonial India). They are tools of control and analysis of suspect and inferior populations.
This is all not to say that data practices cannot be subversive. We have seen many initiatives of activists and human rights organisations using data practices for good, to highlight inequalities and force political change. The difference is in the methods – who designs the processes? Who has control over the information? For what purposes is the data used?
Asking questions of data collection and analysis methods helps us work out who benefits, as well as how and why. Gaps in research are a manifestation of a political choice. Marginalised communities can be served when we look at which tools are being used to keep communities down.
People aren’t one thing at once
MA candidate and campaigner in technology Temi Lasade-Anderson told The Canary:
We know that in the UK, when it comes to reporting on racialized people, the umbrella term “BAME” is used. If data is collected using such a term it can skew our understanding of issues. e.g. we know that it was Black people — the B in BAME who were disproportionately impacted by COVID—19. In research, it’s important to understand intersectionality — that is how class, race and gender intersect, often resulting in harms compounding, and meaning that a poor, Black woman will have a different experience than a middle-class Black woman. It’s imperative to see these intersections to understand inequalities — and when umbrella terms like BAME is used — they obfuscate that.
We’ve used People of Colour throughout this series for reasons of ease. But ease is not the end goal. Good research must be specific and seek to be better. Disabled People of Colour will also face issues of gender, class, and sexuality. Research must be alive to these intersections if policy decisions are actually going to help the most vulnerable members of our societies.
Lasade-Anderson also pointed out that:
It’s really important to note that it’s incredibly difficult to be completely “free” from bias. How you decide to code your date, the categories you use, it’s not a neutral process. You’re making decisions — this may be because of resource pressures; however, data stakeholders, and the work they do, are not neutral. It is a myth that science occurs from a place of objectivity.
Numbers and data are not value neutral. They don’t convey objective truths simply because they are quantitative. The people using tools to sort through data and present findings are not objective or unbiased because people are not objective and unbiased.
It is not an accident of practicalities in data that disabled People of Colour are missed out. It is a manifestation of white supremacist and racist standards in society.
Featured image via Unsplash/National Cancer Institute
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