Oxford Recommendation Letter Example: Ethnography researcher to migration policy (Score 92)

I am an Associate Professor of Public Policy and Social Impact at , and I supervised the applicant's undergraduate thesis over approximately eight months during the [academic year]. That supervision gave me sustained, close contact with their thinking — not just the finished product, but the reasoning behind methodological choices, the quality of their responses to critique, and the intellectual honesty with which they handled the limits of their own evidence. The thesis examined transit migration through [region], drawing on ethnographic fieldwork and secondary policy sources to analyse how informal migrant networks interact with formal border governance. My role was to push the applicant to be precise about what their qualitative material could and could not support. That pressure is where I learned most about them. In our third supervision meeting, the applicant presented a draft analytical chapter that leaned heavily on a single set of field interviews to make a structural claim about policy failure. I asked them to walk me through the inferential steps — specifically, what the interview data established versus what it illustrated. The response was not defensive. They paused, re-read the relevant passage aloud, and said, with some frustration directed at themselves rather than at me, that they had conflated the two. What followed over the next two weeks was a revised chapter that separated descriptive ethnographic observation from policy-analytical inference, added a brief methods note acknowledging the sample constraints, and introduced a secondary source comparison to anchor the structural claim. The revision was not cosmetic. It showed that the applicant understood the epistemological distinction I had raised, not merely that they had moved sentences around to satisfy a supervisor. A second scene is worth describing because it shows a different capacity. Midway through the project, the applicant brought me a draft policy memo — a parallel piece they had written independently, translating their thesis findings into recommendations for a regional migration authority. I had not assigned this; they had drafted it because, as they explained, they wanted to test whether the analytical work held up when stripped of academic hedging. The memo was uneven. The recommendations were clear and the framing was confident, but the evidential grounding was thinner than the thesis itself, and one recommendation rested on an assumption about administrative capacity that the fieldwork did not actually support. I told them this directly. Their response was to revise the memo and, more usefully, to bring the capacity assumption back into the thesis as an explicit limitation section. That kind of cross-pollination between a formal academic document and an applied output is not something I see often at undergraduate level. I want to be honest about the limits of what I observed. The applicant's quantitative exposure during their degree was limited. Their thesis was qualitative throughout, and while they engaged thoughtfully with mixed-methods literature in the review, they did not apply quantitative techniques themselves. For a programme as methods-intensive as the MSc in Social Data Science at Oxford, this is a genuine gap, and I think the applicant knows it. What I can say is that their response to unfamiliar methodological territory in our supervisions — when I introduced them to structured comparison or asked them to engage with a regression-based migration study as a counterpoint source — was curious rather than avoidant. They asked specific questions about what the quantitative findings could and could not establish, which is a reasonable starting point. Whether that curiosity translates into the technical fluency the programme requires is something Oxford will be better placed to assess than I am. What I am confident about, based on direct observation over eight months, is that the applicant revises under intellectual pressure rather than around it, distinguishes between what their evidence supports and what it suggests, and can move between academic and applied registers with genuine purpose. Those qualities matter for rigorous social science regardless of the methods toolkit. I recommend them for the MSc in Social Data Science with the honest note that the quantitative transition will be the real test, and with confidence that they are the kind of student who takes that kind of test seriously. Please feel free to contact me at if you would like to discuss this application further. [Date]