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Queer Data: Using Gender, Sex and Sexuality Data for Action

Book by Kevin Guyan · 22 quotes · Data, Activism, Data Analysis

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Queer Data: Using Gender, Sex and Sexuality Data for Action Quotes

“Heightened data competence can therefore ensure data is used to improve the lives and experiences of LGBTQ people rather than only serve the interests of, what Catherine D’Ignazio and Lauren F. Klein described as, the three S's: science (universities), surveillance (governments), and selling (corporations).”

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“Hacking described his research interest ‘in classifications of people, in how they affect the people classified, and how the affects on the people in turn change the classifications.’ Hacking labeled the subjects of these studies ‘moving targets’ because researchers’ investigatory efforts change them in ways so ‘they are not quite the same kind of people as before.”

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“Considering the Scottish census through these theoretical lenses, where the census is not a neutral representation of a reality but a tool to construct a governable population, raises questions as to whether the census is an exercise in knowledge construction or a tool to bolster the state’s capacity to manage its population. These two objectives are not exclusive: improved knowledge likely facilitates the design of more efficient ways to coerce, control and discipline people who live within a state's jurisdiction. However, if the construction of knowledge is no longer the primary purpose of a census, this throws into doubt then need for a census to collect accurate information that authentically represents the lives and experiences of the people about whom the data relates.”

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“The cleaning of data can remove its queerness: paper surveys where respondents score out the response options ‘female’ and ‘male’ and write their own answer, interview recordings were participants flip the focus and ask questions of the researcher, census returns where LGBTQ couples identify themselves as ‘married’ even when governments do not recognize same sex marriage. These examples demonstrate how collection methods can fail to restrict how participants share data about their lives and experiences. … cleaning, which involves the removal of data that breaks established rules”

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“Although the designs of the trans and gender identity questions in the Scottish, English and Welsh census differ, they both attempt to navigate the same ambition: avoid use of the term ‘cis’. Other census questions, related to Identity characteristics, ask respondents to select the option to which they most closely identify. Questions require respondents to confirm an identity (for example, ‘I am white Scottish’) rather than negate an identity (for example, ‘I am not Scottish Indian’). The design of the trans and gender identity questions depart from this approach are they require the majority of respondents (those who identify as cis, estimated to be around 99 per cent of the population) to answer the way that negates and identity (‘I am not trans’). Ashley has noted how, in English, ‘currently, no word exists in our vocabulary for the broad category which includes being trans and being cis”

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“Do you need more data? Do not assume the need for more data -- enough evidence of a problem might already exist to justify the need for action. Also explore who is already engaged in data practices on the topic to see if resources could support existing initiatives rather than create something afresh. The collection, analysis and use of data are resource-intensive. Before work begins, you therefore need to ask if this is the best use of time, resources and energy to address injustices that face LGBTQ people.”

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“Do you elevate LGBTQ lives and critically examine the invisibility of majority characteristics? One of data’s strengths is its power to tell stories, which can shifts hearts and minds and encourage others to take action. However, increased visibility alone is not enough. A queer approach also problematizes the distinction between the center and the margins so the invisibility of majority identity characteristics, such as cis and heterosexual, are brought into focus and critically examined.”

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“Are your ways of working open, accessible and transparent? Traditional approaches to quantitative data collection and analysis are misunderstood as an objective account of reality; an assumption that masks decisions made throughout the design process. A queer approach to data is also influenced by biases and assumptions; those engaged in queer data practices therefore need to describe how decisions are made, in accessible language, and its effect on the results presented. Openness about the limitations of data helps ensure that an undercount or misrepresentation of data about LGBTQ people is not used undermine political and social advances.”

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“Are damaging data practices and systems capable of reform? Re-evaluate your relationship to data and assess whether existing practices and systems are capable of reform. If reform seems possible, question who is best placed undertake this work. When reform fails, or efforts to reform risk keeping a damaging system alive for longer, consider if an abolitionist approach might put data in the hands of those most in need.”

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“The aggregation of LGBTQ groups offers a response to when small numbers is used as an excuse for an action. however, if research is conducted into the experiences of LGBTQ people, and the number of cases is smaller than anticipated, organizations might also use data to Halt initiatives or cut funding. small numbers therefore presents multiple dangers for the analysis of data about LGBTQ people.”

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“Gilborn et al. have described how the provision of too few ethnic categories [too much lumping] produces meaningless results but the provision of too many categories [too much splitting] can be almost as bad. … [with too few people in each category] the school reported no significant difference in attainment between ethnic groups.”

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“What is your end goal? The collection and analysis of gender, sex and sexuality data is not an objective in itself, nor is the ambition to gather ‘good data’ or fix the numbers. While paying attention to the potential for methods to misrepresent or exclude, such as strategic essentialism, ensure that data about LGBTQ people is ultimately used to construct a social world that values and improves LGBTQ lives.”

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“Do your methods present an authentic account of LGBTQ lives? Rather than adopt methods that promise a tidy dataset, recognize that data about identity characteristics is leaky, pluralistic and can change over time. A queer approach involves the use of innovative collection and analysis methods, such as multiple response options and the provision of open-text boxes, to produce a more authentic reflection of lives and experiences.”

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“Who makes decisions about data that impact LGBTQ people? Decisions that disproportionately affect LGBTQ communities should be made by LGBTQ people. Where this is not practical, or there is a risk of overburdening a small number of people, decision-makers need queer data competence and the ability to recuse themselves when deliberations stretch beyond their capabilities. Use these instances to make space for people with knowledge and experience of the issues under discussion.”

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“Does your project create more good than harm? And for whom? Assess what your project intends to achieve and its potential to cause harm; only continue when the potential benefits outweigh the potential dangers. Disaggregate the differential impacts among LGBTQ people to ensure that the project does not only benefit the least marginalized individuals, for whom sexual orientation is the only characteristics that excludes them from full inclusion.”

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