Bokus
Kevin Guyan, Anthony Mandal - Queer Data, Häftad

Häftad, Engelska, 2022

Queer Data

Using Gender, Sex and Sexuality Data for Action

Av Kevin Guyan, Anthony Mandal

Del i serien Bloomsbury Studies in Digital Cultures

311 kr

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Beskrivning
Data has never mattered more. Our lives are increasingly shaped by it and how it is defined, collected and used. But who counts in the collection, analysis and application of data?This important book is the first to look at queer data – defined as data relating to gender, sex, sexual orientation and trans identity/history. The author shows us how current data practices reflect an incomplete account of LGBTQ lives and helps us understand how data biases are used to delegitimise the everyday experiences of queer people.Guyan demonstrates why it is important to understand, collect and analyse queer data, the benefits and challenges involved in doing so, and how we might better use queer data in our work. Arming us with the tools for action, this book shows how greater knowledge about queer identities is instrumental in informing decisions about resource allocation, changes to legislation, access to services, representation and visibility.
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Kevin Guyan is an equality, diversity and inclusion (EDI) researcher based in Edinburgh, Scotland. He is currently Head of Knowledge and Research at Advance HE, a higher education agency that works to improve EDI for staff and students in universities and colleges in the UK.

Recensioner i media

Kevin Guyan’s Queer Data, though not a quick read, is very comprehensible to an average reader and is absolutely chockablock with ways to understand how research is conducted and how it systematically discounts queer people (or counts us incorrectly, or codes us incorrectly, or…). If you ever do research on anything involving people—even something as minor as a brand-preference survey—you must read this, absolutely. But even the lay reader with no research aspirations will find so many ways to prove that their homophobic cousin Karen is just plain wrong.

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