Research journeys are often shaped by moments that redefine how scholars think about inequality and social change. For CAnD3 Fellows Jing Zhang and Goodnews Oshiogbele, those moments inspired research agendas focused on uncovering hidden disparities and understanding how social structures shape opportunities. Through CAnD3, both have strengthened their analytical tools while engaging with a community committed to rigorous, equity-focused population research.
To start, tell us a bit about your journeys. Was there a defining moment that influenced the way you approach research? And how has your experience with CAnD3 shaped that journey?
Jing:聽A pivotal moment in my research journey was realizing that 鈥淎sian American achievement鈥� looks very different depending on how finely we measure it. Seeing how much variation is hidden within a single aggregated category pushed me to center de-aggregation and multidimensional outcomes in my work. That idea is also at the core of my broader research agenda on stratification: who gets ahead, who gets stuck, and how measurement choices can reveal or obscure inequality.
CanD3 has helped me reinforce that purpose by strengthening my toolkit for rigorous, transparent analysis and clearer knowledge mobilization. Training sessions and community conversations that emphasize responsible measurement, equity-centered interpretation, and reproducible workflows have been especially helpful as I work across multiple datasets and outcomes and try to communicate results that are both precise and accessible.
Goodnews: My research path began with a foundation in the field of management science as an undergraduate student at the University of Benin (Nigeria), where I studied Business Administration and developed keen interests in overseeing effective and efficient administrative operations and promoting ethical marketing. However, the pivotal shift occurred during my Master鈥檚 in Public Administration under the Population and Development program at HSE University (Russia). It was then I first encountered the course 鈥淕ender and Development.鈥� That course was a revelation. It opened my eyes to the deep-seated prevalence of gender inequality and sparked a commitment to upskill for social change. This inspiration let me to my current pursuit of a PhD in Sociology, where I focus on the economic integration of heterosexual immigrant couples in Canada.
CAnD3 has been instrumental in bridging the gap between my theoretical interests and the practical application of population data. The module on Inclusion, Diversity, Equity, and Accessibility (IDEA) has provided a formal framework for the values that drive my work. It has challenged me to think about how research design itself can be an act of equity. Furthermore, intersectionality has been the most transformative concept for my current PhD work. Through the CAnD3 sessions, I am now thinking more about how gender intersects with immigration status, race, and other variables to shape the unique experiences of migrant couples. Overall, the training sessions and Lunch & Learns have provided me with the conceptual and computational toolkit to analyze complex population data, ensuring that my advocacy for gender equality is backed by rigorous, data-informed evidence.
Let's talk about your work.聽What鈥檚 a recent project, presentation, or milestone that you鈥檙e particularly proud of? What made it meaningful, or perhaps challenging, to complete?
闯颈苍驳:听I鈥檓 proud of my recent publication with Dr. John R. Reynolds (my PhD mentor), 鈥淗ow Mythical Is the Model Minority Stereotype? Asian American Variations in Socioeconomic Achievement鈥� (Sociology of Race and Ethnicity, 2025).
The most exciting part was taking a genuinely multidimensional approach鈥攅xamining education, employment, personal income, and homeownership together and showing that many Asian-origin groups are status-inconsistent across these dimensions rather than uniformly 鈥渉igh-achieving.鈥� The most challenging part was making the story both rigorous and intuitive: once you disaggregate into detailed origin groups, you gain analytic clarity, but you also have to be very careful about comparisons, rankings, and interpretation across multiple outcomes.
骋辞辞诲苍别飞蝉:听That would be my article on The Conversation titled 鈥溾��. I am proud of this work because it is my first direct communication with the Canadian public. With this piece, I advocate for Immigration, Refugees and Citizenship Canada (IRCC) to replace the outdated, generalized "dependant" label with more accurate terminology, such as "secondary applicant," to better reflect the professional contributions and diverse identities of modern migrant spouses because not all spouses or partners of migrants are dependents.
The most exciting aspect of this work is its potential to bring lasting positive transformation of social identities should our decision-makers implement the suggested change.
Every researcher needs a recharge.聽How do you unwind or find balance outside of your academic life?聽
闯颈苍驳:听Outside of research, I love spending time with my guinea pigs鈥攑laying with them, holding them, and decompressing. It鈥檚 a simple routine that keeps me grounded, especially during busy stretches of writing and analysis.
骋辞辞诲苍别飞蝉:听I am a musician, specifically a multi-instrumentalist who plays the drums, piano, and guitars.
Photos Left to right: Jing with her guinea pig, Goodnews playing a musical instrument
Finally, if you could have dinner with any data scientist or researcher, past or present, who would it be, and what burning question would you ask them about their approach?
闯颈苍驳:听I would choose Dr. Rachel E. Dwyer, a tenured Professor of Sociology at The Ohio State University, whose work is central to understanding how the financialization of everyday life produces and reproduces social inequality鈥攅specially through credit, debt, and life-course stratification. Her research on unequal debt burdens (including student debt) closely aligns with my dissertation, which examines racial disparities in student loans and their connections to mortgage access and mortgage terms.
My burning question for her would be:
When you model the student-loan to mortgage connection, what is your best approach for distinguishing 鈥渄ebt as an individual constraint鈥� (cash flow, DTI, credit risk) from 鈥渄ebt as institutional sorting鈥� (product channeling, underwriting rules, and unequal pricing)? In other words, how do you make strong, defensible claims about financialization and racial inequality using household survey data鈥攚ithout overstating what the data can identify?
骋辞辞诲苍别飞蝉:听He would be Jacob Mincer (1922-2006), the father of modern labour economics. He wrote about how family migration often disadvantages the earnings of 鈥渢ied movers鈥� (also referred to as accompanying or trailing spouses). Here is the burning question I would ask him:聽You often looked at cross-sectional snapshots of families. My research tracks multi-cohort evidence over time. Given how fast the Canadian immigration policy landscape shifts, do you think your 'one-size-fits-all' model of family migration is too static to capture the dynamic evolution of the earnings of immigrants (including principal applicants and their accompanying spouses) today?
Together, Jing and Goodnews show how careful measurement and interdisciplinary thinking can deepen our understanding of inequality. Their work reflects the broader mission of CAnD3: supporting emerging scholars as they develop the skills and perspectives needed to translate data into meaningful insights.
