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?
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:
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.
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