Hi everybody! I’m Madeleine, and most people call me Maddy. I graduated from McGill in June with an Honours degree in International Development and a minor in Political Science, and I could not be more grateful to have this opportunity with PODS to kick start this next chapter of post-grad.
Over the course of my time at McGill, my passion for social justice and human rights evolved into an interest in foreign and public policy in the areas of migration and forced displacement, conflict resolution, and climate justice. During these years, I have had the chance to work with immigrants and refugees with the non-governmental organization IRCO in my hometown of Portland, Oregon, as well as with SciencesPo Refugee Help in Paris while on exchange, and with a local organization supporting marginalized women and children in Kathmandu, Nepal. After these experiences working in community-based initiatives alongside people who are facing direct structural injustices, I’m driven to engage in policy advocacy because I see it as a critical tool for affecting sustainable and systemic change.
Growing up, I always considered myself “not a computer person” and never imagined myself working with a computer screen full of code like I am now. I applied to PODS in my final year at McGill because I was beginning to realize the relevance of quantitative analysis in the field, as a key part in understanding the multifaceted dimensions of human rights issues on the ground. But I had no idea the extent to which PODS would open my mind to just how impactful data-literacy is for tackling complex policy problems. And thanks to PODS, I discovered an interest in coding and data visualization that I never anticipated.
As a PODS Fellow this summer, I have been interning at the Institut du Québec (created by Conference Board of Canada and HEC) where I’m conducting an exploratory analysis of social mobility in Québec at the county-level and over the years, using the 1986 and 2016 census data. This has involved calculating social mobility indicators that are comparable across both datasets, and visualizing the changes across these indicators in R. Through this work, I now realize the value of data visualization and mapping to understand initial patterns in your data, but also the extent of value-based decisions that are made at every step along the way.
To this end, the PODS program does an excellent job communicating the delicate balance between the benefits and the risks of data analysis. I appreciate the focus on ethics in data science and AI that the program takes, and this needs to be given more attention in the world of data science today. Looking at the data is a necessary initial step to understanding social phenomena and the possible appropriate policy recommendations for them, but it is essential that this is approached within an intersectional understanding of the socioeconomic and political context. Because numbers can be interpreted so differently and, at best, they only tell one side of the story.
PODS has equipped me with the intersectional knowledge and skillset I need in order to apply myself towards making effective and responsible policy change in the issue areas that I am passionate about. This experience has inspired me to pursue work in data-driven policy advocacy and program development, taking a more wholistic approach to the data analysis that is informing our understanding of social phenomena and our responses to them. Beyond this, I am very grateful for the incredibly supportive team, great mentorship, and wide network that PODS has introduced me to. With everyone’s support along the way, I’ve been able to step outside of my comfort zone and learn skills that are completely new to me. I can’t wait to work more closely with data in social justice and the protection of data rights going forward.
