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Recognising diversity in sexual orientations and gender identities has led to more\u00a0inclusive approaches to data collection.\u00a0 Institutions have made genuine efforts to\u00a0acknowledge these diverse forms of identity. Yet for many LGBT+ people, these efforts\u00a0involve significant trade-offs. Lives are fluid, dynamic, and rarely fixed, while our data\u00a0categories are predefined and\u00a0 fixed in time and space. Coming to understand one\u2019s own\u00a0sexual orientation or gender identity can be a long, personal process;\u00a0 one that does not\u00a0always align neatly with the rigid categories used in datasets.<\/p>\n
We often like to imagine our data as stable and independent from the messy realities of\u00a0lived experience. But many LGBT+ lives\u00a0 resist this static framing. Not everyone is \u201cout\u201d\u00a0in every context. If we are not out to our employers, are we being forced to\u00a0 reveal\u00a0information we would prefer to keep private? Does a bisexual person in a different-sex\u00a0relationship stop being bisexual\u00a0 for a survey? If someone feels uncertain or conflicted\u00a0about their gender identity, how should they report? These tensions\u00a0 create a mismatch\u00a0between our expectations of what data should reveal and what it can realistically\u00a0\u2018capture\u2019.<\/p>\n
To explore these issues, we invited Dr Kevin Guyan, Chancellor\u2019s Fellow at the University\u00a0of Edinburgh, to discuss themes from\u00a0 his book Rainbow Trap. His work examines how\u00a0inclusive data practices unfold across six areas: crime recording, dating apps,\u00a0 border\u00a0controls, cultural industries, fitness activities, and workplace EDI initiatives. In a 40-minute presentation followed by a\u00a0 Q&A session, we had the opportunity to engage and\u00a0discuss these challenges.<\/p>\n
The event was organised for students in Tools and Techniques of Applied Quantitative\u00a0Methods, an MA-level course where\u00a0 students work intensively on data carpentry,\u00a0applied quantitative data analysis, and communication for both academic and\u00a0 general\u00a0audiences. The session was open to staff and postgraduate students, and the\u00a0discussion was dynamic.<\/p>\n
Dr Guyan\u2019s talk offered a shift in the perspective we use to discuss these issues in the\u00a0lecture, with a chance to pause and reflect on the conceptual foundations of the data\u00a0practices we so often take for granted. Participants reflected on issues ranging from\u00a0disclosure and workplace data regulations to navigating classification systems\u00a0embedded in institutional contexts,\u00a0 including government, academia, and industry.<\/p>\n
This event served as an invitation to rethink how we approach data collection,\u00a0production, and analysis. As quantitative\u00a0 researchers -whether in academia,\u00a0government, or the private sector- we must cope with what inclusive data practices\u00a0require. Ultimately, additive approaches, such as simply adding more categories or\u00a0boxes, may not be sufficient. Instead, we need to\u00a0 consider the deeper implications for\u00a0data design, implementation, and governance. Only then can our data practices begin\u00a0to reflect the complexity of the lives they aim to represent.<\/p>\n
It was also an opportunity to bring these conversations into dialogue with our\u00a0department\u2019s EDI lead, Dr Jamie Hagen, who\u00a0 highlighted the relevance beyond this\u00a0single course to our broader research practices as scholars, supervisors, and\u00a0instructors.\u00a0 In doing so, we connect an important academic concern with the everyday\u00a0realities of our work.<\/p>\n
The discussion set an encouraging precedent. It reminded us that inclusion is not only\u00a0about counting more voices, but about\u00a0 ensuring those voices actively shape our\u00a0conceptual frameworks, influence our operational decisions, and have a seat at the\u00a0table when institutional choices are made. The shift from merely recognising diversity to\u00a0embedding it in our analytical\u00a0 foundations has the potential to transform how we teach,\u00a0research, and design our data.<\/p>\n
As we continue developing our curriculum, we plan to include these ideas as accessible\u00a0and essential readings for future\u00a0 cohorts. By including these conversations into our\u00a0teaching, we help prepare the next generation of researchers with the\u00a0 awareness and\u00a0tools needed to build more thoughtful, responsive, and genuinely inclusive data\u00a0practices. In this sense, the\u00a0 impact of the event extends well beyond a single lecture. It\u00a0is a commitment to recognise complexity not as a challenge to\u00a0 overcome, but as an\u00a0opportunity to create better and ethical approaches to data for the world we study.<\/p>\n
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