Intersectional feminism to reenvision mathematical literacies & precarity

ABSTRACT

Current global crises (e.g. COVID-19 pandemic and climate change) necessitate changes to mathematics curricula, especially related to using mathematics to solve real-world problems. We begin with the Programme for International Student Assessment's (PISA) framework for mathematical literacy (FML), since it functions as a global guide for curriculum. We demonstrate its inadequacy to solve current crises and to mediate the precarity of girls and women. Then we reenvision the FML by integrating concepts of critical mathematics education with intersectional feminism. We reenvision how to think about mathematical literacies. In particular, we add practices of feeling, acting, and reimagining to the conventional construct of mathematical reasoning. We reenvision ways to think about or classify real-world problem contexts by exploring three potential themes for real-world problem contexts.

KEYWORDS:

• Mathematical modelling
• real world contexts
• mathematics education
• critical mathematics education
• intersectional feminism

Acknowledgements

This material is based upon work done while Herbel-Eisenmann was on assignment at the United States National Science Foundation. Any opinion, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 “Women” and “men” are mentioned only five and three times respectively in the NRC (2012) report.

2 These are D'Ignazio and Klein’s (2020) principles from data feminism.

3 For instance, D'Ignazio and Klein (2020) discuss the merits of the Anti-Eviction Mapping Project (AEMP), a collective of housing justice advocates in the United States who collect data on home eviction. The data collected by the AEMP is messy precisely because of the large number of evictions and because of the nuanced nature of what it means to be displaced. In contrast, thematically similar data collected by the Eviction Lab based on court-ordered evictions is cleaner and more readily analysable, yet it does not contain nearly the same amount of eviction data as the AEMP.