The data that governs me.

To govern with data, the decision makers have to first collect data sets, for example, through public censuses. Algorithms are fed this data as the starting point of their operations. (O’Neil 2016). These combined form the new tools governing bodies employ in decision-making (0zga 2016).

All of us are parts of these data sets. Programs such as No Child Left Behind (Anagnostopoulos et al 2013) mean that educational bodies pay close attention to those students who traditionally struggle at schools, for example, those with ASD. We strive for fairness and inclusion in education. For that we need stats on the number of autistic students. This affects them directly in the wake of standardised tests (.ibid), something last week’s data showed. It also affects teachers whose job is now evaluated based on test scores, like IMPACT (O’Neil 2016).

This week’s visualisation shows just how much the data is unreliable. It depicts the statistic on Autism prevalence in different groups. Depending on the source, the numbers vary. The difference might seem small. For any given school, it’s a handful of students. But If we multiply it by the number of schools, we get hundreds of students who are, well, left behind, and for whom schools aren’t getting funding. More importantly though, if the data is so unreliable, how can decisions made based on it be fair, trustworthy, and right?

(Employment data unavailable for most countries, and only gathered by a handful of organisations SOURCE)

BONUS:
Until recently, I was not even a part of the official data sets. Like most women on the spectrum without clear impairment (Rynkiewich et al 2019), I was diagnosed later in life:

Sources.

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.

APA By the numbers: Autism rate increases

CENTRE FOR DISEASE CONTROL ASD Data & Statistics Data & Statistics – Prevalence

Hines, E. Rates of Autism Spectrum Disorder Diagnosis by Age and Gender at AUCD

Loomes R, Hull L, Mandy WPL. (2017)What Is the Male-to-Female Ratio in Autism Spectrum Disorder? A Systematic Review and Meta-Analysis. J Am Acad Child Adolesc Psychiatry. Jun;56(6):466-474. doi: 10.1016/j.jaac.2017.03.013. Epub 2017 Apr 5. PMID: 28545751

O’Neil, C. (2016) Weapons of Math Destruction. Random House Audio (Audible release date 09-06-2016)

Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81

Rynkiewicz1, A. Janas-Kozik, M. Słopień, A. (2019) Girls and women with autism. Psychiatria Polska

Zhang, Y., Li, N., Li, C. et al. (2020) Genetic evidence of gender difference in autism spectrum disorder supports the female-protective effect. Transl Psychiatry 10, 4 https://doi.org/10.1038/s41398-020-0699-8

2 Replies to “The data that governs me.”

  1. ‘We strive for fairness and inclusion in education. For that we need stats on the number of autistic students.’

    Useful point here that highlights the bind of data. In order to ‘count’ we need to be seen by decision-makers, and this seems to challenge the idea of resisting datafication entirely. I suppose the question then is how much agency we have in the relationship with data-driven governance, and to what extent ‘being in the game’ affords benefits.

    ‘If we multiply it by the number of schools, we get hundreds of students who are, well, left behind, and for whom schools aren’t getting funding.’

    So, perhaps this is a reason to use statistical evidence at regional or national levels, because it highlights the scale of marginalised groups? And perhaps also the inconsistency of current data – is that then a reason for more standardisation? In a way, this kind of standardisation is what HESA (https://www.hesa.ac.uk/) are trying to achieve in the UK, and in England specifically.

    You visualisations, particularly the first one, do a great job of highlighting the issues of being underrepresented. The 1:152/252 seems to be conveyed much more powerfully as a kind of ‘info-graphic’ than simply in numbers.

  2. Yeah, that’s powerful. I like how you used visualisations to do the 1:5, 4, 3 ratios, it really brings out the meaning of the data in a way most graphics just don’t.

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