Data-Driven Equality Lab (DDEL): Enacting Interdisciplinary Strategies for Unravelling Structural Discrimination
The project entails development of the didactic components for establishing a lab specialised in producing evidence of structural inequality. The lab will
connect students across departments and with societal partners and provide a much needed training on gathering and analysing scientific data to aid the
struggle against structural inequality.
Background information
Structural inequality encompasses economic disparities and systemic imbalances embedded within laws, institutions, and cultural norms, affecting various aspects of life. This manifests in discriminatory laws, policies and institutional practices that perpetuate inequalities across racial, gender and socio-economic lines. Examples of such inequalities are the disproportionate sentencing and incarceration rates for marginalized communities, the barriers to quality education that students from underprivileged backgrounds face, unequal access to quality healthcare based on factors such as race, income, geographic location and residence status and various forms of employment discrimination, including wage gaps and glass ceilings, limiting career advancement for women, minorities, and other marginalized groups.
Aims
The aim of the project is to design the didactic components for an interdisciplinary lab focusing on producing actionable data on structural inequality (the Data-Driven Equality Lab) for students of law, economics, and governance.
Project description
In response to the abovementioned difficulties in collecting evidence of structural discrimination, we propose to set up an academic lab where students can be involved in data collection to support stakeholders in their battle against structural inequalities. Such a lab, with expertise on and ways of proving structural inequality that span across laws, economics and institutional structures, could prove an invaluable partner for societal partners. By giving students a role to play in collecting evidence of structural discrimination for societal partners and stakeholders outside the university, the lab can expand the current offer of Community Engaged Learning (CEL) courses within the LEG faculty. This aligns with the faculty aspiration towards societally-engaged education. Moreover, while students within the different schools are trained to conduct research and collect data in methodologically sound ways within the limits of their own disciplines, they are rarely given the opportunity to participate in interdisciplinary research initiatives. The proposed data lab will bring together students from different disciplinary backgrounds and challenge them to combine their skills to address the complexity and multifaceted nature of structural inequality, thus enabling an interdisciplinary learning experience.