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Kevin Guyan on Queer Data

Updated: Nov 15, 2022




How should governments collect personal data? In this episode, we talk to Dr Kevin Guyan about the census, and the best ways of asking people to identify themselves. We discuss why surveys that you fill in by hand offer less restrictive options for self-identification than online forms, and how queer communities are not just identified but produced through the counting of a census. As Kevin reminds us, who does the counting affects who is counted. We also discuss why looking at histories of identifying as heterosexual and cisgender is also beneficial to queer communities.


Kevin Guyan is a writer and researcher whose work explores the intersection of data and identity. He is the author of Queer Data: Using Gender, Sex and Sexuality Data for Action (Bloomsbury Academic), which examines the collection, analysis and use of gender, sex and sexuality data, particularly as it relates to LGBTQ people in the UK. Kevin is a Research Fellow in the School of Culture and Creative Arts at the University of Glasgow. In 2016, Kevin completed a PhD in history at University College London and has written about LGBTQ data for publications including WIRED, The Independent and The Scotsman. He is a member of the Royal Society of Edinburgh’s Young Academy of Scotland, sits on Young Scot’s Data Advisory Group and the boards of Evaluation Support Scotland and the Equality Network.


Reading List:


Guyan, K. (2022) Queer Data: Using Gender, Sex and Sexuality Data for Action. Bloomsbury.


Ahmed, S. (2012) On Being Included: Racism and Diversity in Institutional Life. Durham: Duke University Press.


Spade, D. (2015) Normal Life: Administrative Violence, Critical Trans Politics, and the Limits of Law. Durham: Duke University Press.


Transcript:


KERRY MACKERETH:

Fantastic. So thank you so much for joining us here today. Could you tell us a bit about who you are, what you do and what boys are thinking about gender, feminism, technology, and specifically queer data?


KEVIN GUYAN:

Hello, well, thank you for having me on the podcast. My name is Dr. Kevin Guyan. I'm a Research Fellow at the University of Glasgow. And my research very broadly covers what happens at the intersection between data and identity. And how I arrived at this spot - so for a long time, I've been working on issues around gender, feminism, equality and diversity, both in academic contexts, as well as in practice working with practitioners across the higher education sector in the UK. And in terms of technology, I would say, I kind of landed in this space fairly recently, maybe in the past three years, and mainly through an interest in the Census in Scotland, where I'm based, and how the design of questions around gender, sex and sexuality are kind of being designed through a process in Scottish Parliament. And that really kind of piqued my interest into how particularly quantitative data and identity intersect, and what are the ramifications of that for LGBTQ communities. So since that design moment around the census, we've seen a lot more interest in how data about the wider LGBTQ communities in the UK is conducted, and some of the fallout from that in recent years as well. So that's kind of how I've landed in the space that I’m working in at the moment.


ELEANOR DRAGE:

Well, I very much look forward to hearing you speak more about the sensors, because it's something that a lot of people were talking about, recently. And there's so much debate about what kind of data is collected and whether that data should be collected in the first place. But before we go into that, can you help us answer our billion dollar questions? So what is good technology? Is it even possible? And how can feminism help us work towards it?


KEVIN GUYAN:

Wow, what a big, big question. Is good technology possible? For me, maybe we're kind of asking the wrong question. If I think of technology in my work around data, and I speak quite a lot about good data, bad data and what do we mean by these types of concepts - whether or not aiming for good technology is actually the outcome that we should be striving for if we're looking to use technology purely as a means to change and impact people's lives and experiences. In my work around data, I kind of say the data on its own doesn't do much to really impact the lives of LGBTQ people. It's used as an evidence-base or it's used as a tool for political and social change. And perhaps with technology we could apply the same arguments where whether or not the technology is good or bad depends on its application and its use, whether or not ethically or morally, we're kind of making judgments about its goodness or badness. So maybe - that's me kind of cheating slightly by flipping the question on the head, actually, whether that's the right question we should be asking. In terms of feminism, and definitely, I think it's central to all these discussions, I'm thinking through what this means for gender, sex and sexuality across all areas of technology or data systems, data practices, I think is really key, particularly to kind of shine a light on the biases of those architects and designers behind the systems and structures, which are overwhelmingly sis heterosexual, masculine, non-disabled. And I think really exposing some of those biases is what I hope to do in my work around technology, and hopefully how I use feminism in my work.


ELEANOR DRAGE:

Super. So now from the abstract to the concrete. A lot of data is collected about the negative experiences of LGBTQ+ communities, or is used to paint those communities in a negative light, because they show them to be highly disposed to criminality and psychological maladjustment. So going forwards, how can we build data systems that meaningfully acknowledge this harmful history and ensure that it's not perpetuated?


KEVIN GUYAN:

For me this has been really at the top of my mind in the past few months, particularly with LGBT History Month in the UK and thinking through the kind of connection between history and data and contemporary LGBTQ lives and experiences. And what has kind of surprised me in lots of our current kind of contemporary discussions about the need for more LGBTQ data is often overlooking the history of these data practices, and how LGBTQ communities alongside communities of colour, alongside women, alongside disabled communities, have historically been minoritized and marginalised through data practices. The use of data in many of these examples is a tool to further exclusion, to further difference, and to create evidence of difference, which is ultimately used to cause harm. I've written some pieces in the past few months, around how if you look at the past 50 years around data on gender, sex and sexuality, and how that's been used to police and to exclude LGBTQ groups, as you mentioned, whether through evidence of criminality, in criminal records, evidence of being psychologically wrong, or maladjusted, alongside just a general sense of positioning these groups as different from the norm. So I think when we think about contemporary ideas about data and wider ideas of technology, we need to really be mindful of these harmful histories. And it always annoys me when people describe LGBT groups that may be hard to reach in data practices or data collection exercises and not really thinking through why these groups for a variety of reasons, might be reluctant to share information with police, with law enforcement agencies with the government due to these harmful paths and harmful histories. So I think when we're thinking about how we're designing systems today, it's impossible to do that without looking to the past.


KERRY MACKERETH:

Fantastic, yes, I think this really resonates with a lot of Eleanor and I's work. It certainly resonates with my work, thinking about anti Asian racism and AI are kind of more broadly thinking about the intersections of race and gender and technology that we can't really understand a lot of these contemporary tech phenomena, or these contemporary relationships with data without thinking about their historical roots. And so I also want to ask you a bit more about the census as well, like Eleanor, which was thinking about issues to do with disclosing data related to your gender or sex or sexuality. And so this is something I think about a lot as well in relation to racial identities and the kind of complexities of self identification. And so we want to ask, what do you think are the pros and the cons of collecting this kind of sensitive data, and also asking people to self disclose or describe their own identities?


KEVIN GUYAN:

A really interesting subject, which I think has maybe come into the more mainstream, wider consciousness in the past few years. And sadly, I think a lot of discussion around self-identification, particularly the context of UK has been pushed through through the prism of trans-exclusionary campaign groups, and work around kind of self identification along lines of gender and sex. But kind of zooming out, self-identification is a far broader question, a far broader issue, which has really been how a lot of areas of equality and diversity workers have operated and managed in the context of the UK for at least the past 10/20 years. And what I find really interesting is how in my work, which is mainly around data in social and cultural studies - so things like I mentioned, work in the higher education sector around maybe the experiences of staff and students, or national surveys, or population level surveys, like a census - in these types of exercises, I do think self-identification is the best approach for those types of exercises, seeing that all approaches to any collection of data about identity is going to be hugely imperfect. And so I wrote a piece earlier this year, where it was kind of describing how we capture data about identity as maybe being comparable to the metaphor of a bug catcher and catching butterflies with a kind of broken net, and how when we're thinking about identity, these ideas that are moving in time and space, they're changing across society, they're changing within individuals who might have different identities at different time points, and thinking about how we collect data as being this very imperfect process, isn't a failing in itself. But what I do find more problematic is sometimes when people use as a metaphor of data and identity ‘being like an archaeologist’ uncovering relics, which are kind of long borrowed, and they're there. They're static, they're fixed in time and space. And it's just a case of finding the right tools to uncover them. And for me, that's not how identity operates in the real world. And I think when we're thinking about all types of data collection, whether it's qualitative or quantitative, we need to have the tools in place to be able to handle the fluidity, the subjectivity, and the movement of these categories. And alongside self-identification, in my book, I describe other ways in other contexts, we have things such as external identification, where somebody else is ascribing identity characteristics to an individual historically, this is often the case in surveys where somebody might make judgments about somebody’s gender based their voice, for example, or somebody’s race based on how they perceive their skin colour or their appearance. And there's a lot of obviously problematic dimensions to this around agency - around who's making the call around how somebody identifies. And slightly more outside my space of work we've got things like biometric data, where bodies are being read to try and make judgments about someone's identity. And problematically, there are some studies in this space around whether you can tell if somebody is gay or not by how their face looks through your kind of scanner, and these types of studies, which are, are interesting, but often extremely problematic. And then, fourthly, a big area, particularly in online spaces is around what I describe as behavioural data. So for your data about identity isn't self-disclosed, it's not necessarily externally prescribed. It's more about how we use and engage with platforms, websites, things like Netflix, Facebook, for example. Netflix has never asked me about my sexual orientation. But I get a lot of recommendations for watching LGBTQ films, because they can discern for my viewing habits certain ideas about how I identify. I think, for me, it's a really interesting area going forward and probably the biggest growth around data identity technologies moving to, but as with all other means, methods, extremely imperfect, and problematic.


ELEANOR DRAGE:

Yeah, that's so interesting, and a lot of stuff that people probably have experienced at home. I have friends that really disagree about whether they find it a bit scary that Netflix is suggesting stuff that actually corresponds to the the way they identify. But on the other hand, people who are like, No, it's great, because I actually like the stuff that I'm suggested. So the kind of double edged sword of…


KEVIN GUYAN:

Double edged sword definitely, for me, it's a matter of convenience sometimes. But at the same time, I read a really interesting piece on the website Openly about the dangers of digital footprints for LGBTQ communities in some other geographies and geographical contexts. So the example of gay men in some Middle Eastern countries, and having this digital footprint on Whatsapp, on Grindr, on Facebook Messenger, for example, and how this can be used as an evidence base of criminality, of deviance. And again, even if you're not self-disclosing certain information, these digital footprints can have real world ramifications, and I think dangerous for a variety of different queer communities.


ELEANOR DRAGE:

Yeah, absolutely. And we'll ask you about that as well. What you were saying earlier about, you know, the fluidity of, of identity, and what the way people self identify, do you think there is any scope for the census to be better in how it collects information? How could it do that?


KEVIN GUYAN:

Where there might be some growth if a census happens again, every time a census happens, which in the UK is every 10 years, there's always debate and discussions about whether this will be the last census, and there's many, many countries in the world, which do population level counts of their population without doing kind of standardised census, this is done through administrative data and other types of data collection practices. And where there might be some growth is around other means of describing your identity through things like write in boxes, and free text boxes, I think this is a really interesting space for growth, and particularly for sexual sexualities for different races, ethnicities, and religions, these groups which maybe don't fit into a kind of drop down list of five or six choices, and giving people the space to kind of describe how they want to identify in their own words, and the kind of analytical practices behind that are improving. So I think, I'm sure we've engaged in many studies where there's a write in box, but whatever is written into that box isn't actually analysed and just ends up either being grouped as ‘other’ or are ultimately thrown in the bin, which, for me, kind of defeats the point of actually even asking the question if we're not going to use the data in any meaningful way. Increasingly, that our means of analysing that write in data, and actually aggregates meaningfully, disaggregates meaningfully, and tries and removes the subject of decisions made about how to group and categorise those responses at each stage of analysis, and the Census does have that option, so in the context of Scotland, for example, for the sexual sexual orientation question, the response options are lesbian, gay, bisexual, straight, heterosexual, and a right hand box. And I've been told that data will be analysed meaningfully. So it will be interesting to see how that self-disclosed information is used and how that kind of what emerges through that type of data collection after the count is completed.


ELEANOR DRAGE:

I have a friend who works at the UN, by the way, and she was talking about how they were trying to collect different kinds of information on gender identity. And then she was so proud of all the data they collected, and they just couldn't put it into the system, because there just weren't options to record that data. And the frustration of that being the case kind of across the board at very much limits what these hugely powerful NGOs can record and the kind of data work they can do.


KEVIN GUYAN:

I think it's a really interesting space as well. And I think often people overlook …. In my book, I write about the collection of data, the analysis of data and the use of data. And I think we speak a lot about how data is collected, how we design methods, how we design systems, we don't speak as much about the analysis of data. When it comes to the cleaning of data when it comes to - actually we have this amazingly large but messy data set - it ultimately goes to a team of people who might know a little bit or a lot about the data in question, who are ultimately tasked with making decisions about how to clean the data, how to aggregate certain groups, and how to maybe group together whether or not accounting polysexual, and pansexual as distinct categories are the same categories, bi and bisexual as similar or different, queer … these types of decisions in the analysis stage can be quite can give a lot of power to those tasked with analysis.


KERRY MACKERETH:

That's really fascinating. And actually one of our other guests on the podcast, Catherine D’Ignazio who wrote data feminism with Lauren Klein talks about kind of these issues to do with sort of cleaning the dataset kind of drawing out the sort of eugenicist roots of that concept. But also thinking about this idea of ‘strangers in the dataset’, what happens when someone who's not familiar at all with a particular set of data from a community, that's come and tried to create boundaries, create categories. And I guess if you don't mind diverging from the script a little bit, I want to ask you about thinking through these things around self identification, but also sort of the different kinds of uses of these data collection technologies, about the possibilities for like resistance. And the ways you've seen people kind of try and subvert this some my earlier research focused on the British suffragettes and, you know, one of the things they did was that they had kind of census protests, so that either refused to you know, further centres because they weren't counted as persons or, you know, one suffragette, I believe was Emily Wilding Davison broke into the house of the parliament so that she could try and, you know, put her address as the Houses of Parliament on the Census. And so there are all sorts of networks set up for people to evade the census, largely kind of male suffragettes who are like housing, female suffragettes, to sort of hide them from the law. But yes, I just be interested to hear like, to what extent to these kinds of resistance practices play out in the work you do?


KEVIN GUYAN:

Really, I love that question. The reason being is that I do speak about this in my work around how data collection practices can be a site of resistance. But what I've seen changing is increasingly as a move towards digital collection tools and digital methods, whether it'd be a census, or a university research project, or a staff diversity monitoring survey, and it's increasingly rare now to be given a paper copy of anything to complete. For example, censuses this year, and last year, are digital first, so very much the plan is for most people to be completing these things online. However, that does remove a key site of resistance and subversion. And historically, where it would be far easier to write in your own response option, whether it be writing for non-binary respondents for example in the census, not ticking the sex, male or female, but kind of scrolling the mouse and writing non binary, that's possible in a paper survey, that's not possible on an online website where you can only tick one of two boxes before proceeding to the next page. So increasingly, some of the sites of resistance through the actual methods we're using are being removed in a race for a variety of reasons. Which is a shame because I think even historically, there's been a lot of really interesting uses of those sites of data collection to affirm your identity, to state your identity, to kind of describe how you want to be described in your own terms. But with that move online it does remove some of these possibilities, and potentials for resistance and subversion, which are really key to driving forward how we want to use data in a way that both we're sharing data about ourselves in ways that fits rules, but also breaks rules as well.


ELEANOR DRAGE:

It's so interesting that the form and the medium really affects the kind of data that's collected. And who knows, maybe when we're filling out these forms in the metaverse, there will be different opportunities or less opportunities to be able to express yourself, I guess that feeds into this point of, for people listening who don't identify as LGBTQ+, and maybe do work with data, how can they get involved? And how can they change design practices, so that they can positively impact the kinds of issues that we're talking about.


KEVIN GUYAN:

So I think there's a variety of ways I think, in my work, I try and draw or kind of, not come down on either side about the role of people who don't themselves identify as LGBTQ in practices related to LGBTQ data. And more broadly, my work my work looks at gender, sex and sexuality data. I'm really keen in our work to be highlighting how the data about three heterosexual people has a history, has a politics, has a design to it, a rationale. And similarly, data about people who identify as cisgender is not apolitical or ahistorical either, it has a history to it, and trying to shine a light on these categories, which have been positioned as the centre, as the norms of our studies, and actually shine kind of a bright light on them as well really helps inform both our worker and LGBTQ communities, but also the broader querying and asking questions about the methods we're using for data more generally. And in regards to the rule, the people who don't identify in these ways the role they can play in data practice around LGBTQ groups: what I always kind of like to argue is it's across a wide range of different identity questions around equality and diversity themes. It's not a requirement to have lived experience of the topic under discussion to be involved in the design and management of these practices. Why I've kind of landed at that point are my concerns about the overburdening of people in minorities and marginalised groups. And I think through, for example, designing a workplace diversity monitoring form. So diversity monitoring is really common across a wide range of businesses and universities and governments councils. And basically, if an organisation wants to make sense of its workforce, and it's usually aligned with the nine protected characteristics in the Equality Act, where it would have concerns is if, whenever it comes to design your question on, for example, the lives and experiences of trans employees or trans staff that maybe one or two trans staff in your organisation are really relied upon to be single-handedly designing this question or designing this research exercise on top of I'm sure 101 other equality and diversity tasks within your workforce, on top of their day job as well. And I think this kind of demand of the labour of people in minoritized groups to be single-handedly leading on this work is problematic. At the same time, and they certainly should be playing a key key role in the design of questions or decisions which disproportionately impact upon them. I think it's getting that balance between when it’s your space to step up and push forward change, at the same time, not overly relying on people with lived experience to single handedly drive action and change. So yeah, I think it's a great area, I would say in some spaces, there's not enough LGBTQ voices in the room. I'll speak a bit more later about how certain questions on the census were designed, and often without people who identified as trans in the space, and designing questions on trans lives and experiences. But at the same time, I think people can have expertise and some competence in experiences that do differ from their own their own lived experience.


KERRY MACKERETH:

That's so fascinating. And it's certainly something I think that really resonates with the kinds of challenges we face as well in our work, which is… I think it's very interesting as well, when you come to kind of the groupings like, you know, as you pointed out the under representation of trans voices under the LGBTQ+ umbrella is certainly something I experienced as kind of like a multiracial or racialized person, which is that often you're in this really weird circumstance where you’re thinking, Oh, I'm the only person in the room here that is technically racialized person, but I'm a really white-presenting super privileged person and so it doesn't also feel right for me to somehow be kind of trying to fly this particular flag. But then who else is going to do it? Because who else has that particular set of lived experiences? I think finding that balance, as you talked about, is really important, but also can just be really challenging not only just in terms of the emotional, physical labour, but also in terms of how you locate yourself in all these different power relations.


KEVIN GUYAN:

Yeah, and I think certainly in regards to progress as well, I think it's a really big issue in academia, among staff in higher education, particularly at the senior levels, professors, for example, the tiny number of Black women professors in the UK, if they were to be involved in every decision around senior decision making, around impact, things that impact Black women in academia, they would have new time for research, they would have no time for leading on projects of writing articles and doing stuff that's ultimately going to advance their research career. And I think thinking through the implications of demanding that people who already minoritized for a variety of reasons give more and more of their time needs to be thought through before we go with the assumption that only people with that lived experience can make judgments about that question.


KERRY MACKERETH:

Yeah, it's also so hard because its like a snake eating its own tail, we're also starting to see that kind of disempowerment through that narrative or certainly saying like, Oh, you know, we wanted to ask you to do this thing, but we didn't want to give you too much was because like, we know, we don't want all the labour to like full on to minoritized people, but then you're like, oh, but actually, that's a project maybe that I would have taken on because I think it's important. And so it does feel a little bit like a rock and a hard place sometimes.


KEVIN GUYAN:

Definitely


ELEANOR DRAGE:

I want to do something very mean and asked you to explain what a norm is, as well.


KEVIN GUYAN:

I guess there's probably a variety of definitions for norm in the example I used earlier in the podcast. It was kind of around people whose identities are maybe treated as invisible, but not invisible in that they're kind of hidden in the shadows, but that they're so unexceptional that they're just assumed to be the default assumed to be the standard. So when it comes to the design of the technologies and data systems, often we may position the white male non-disabled cis-heterosexual man as the kind of default norm in that system.


ELEANOR DRAGE:

And so just to finish on, I do a little bit of work on looking at how biometrics for example, don’t just identify a person but actively produce them. And also Kerry and I look at hiring technologies and how they don't just identify an ideal candidate or identify your personality from your face, but actually produce those characteristics, according to - as you've just described very beautifully - the norm. So can you explain how that relates to your work too? And also, you've said that the work of Dean Spade has been hugely influential to you, and the way that they've talked about deserving and undeserving groups, and how that idea is created, according to the norm, or what is normal? Can you explain that a bit better!


KEVIN GUYAN:

Yeah, that is a really good question. Thank you. And for me, it's a really interesting area of work and I think really, I guess, maybe the area of my research which excites me the most around this kind of this intersection of data and identity, and how it can actually challenge some of the celebratory narratives around being counted, some of these narratives around inclusion and diversity, and actually kind of position some of this work as a bit contrarian, or as a bit critical of the more kind of mainstream liberal approaches to equality, diversity and inclusion. So I guess there’s a few strands to your question there, the first strand around how the tools and methods and practices that we use, whether it's across wider technology, or in my work, around data, how these tools and methods don't just record or report something, they're actually involved in constructing the thing that they're observing. So this is very much coming from broader work in the field of science and technology studies about how the methods aren't just recording stuff, they’re actually producing and constructing something. So I wrote an article last year around how the census in Scotland was doing more than simply recording an LGBTQ population. It wasn't just kind of going out and uncovering something that was sitting there waiting to be counted, but actually, through the design of the questions, and the engagement, the work of stakeholder groups, the presentation of the questions, the Scottish census and the wider Scottish Government, we're actually constructing a queer community. However, in my work, I argue that the community that they constructed was ultimately something that made sense to those in existing positions of power. Unsurprisingly, in a government, in a parliament, those in positions of power are overwhelmingly and cis, and overwhelmingly heterosexual. And the trickle down effect of that is that the queer community that are produced through the counting of a census through the questions, through the response options, through what's included, what's excluded, ultimately, is something that needs to make sense to, and ultimately palatable to those in positions of power. So this tension really became clear to me when I looked at how there was generally really no pushback little issue about the counting of lesbian, gay, bisexual people in the census. I could count on one hand, the number of objections that came across from politicians, or from campaign groups around the counting of these communities, whereas there's a lot more pushback and a lot more challenges when it came to, for example, the counting of non binary people. Ultimately, there was some discussion about whether or non binary question would be added on sex. Ultimately, in Scotland, the decision was made not to change the question to count non-binary people. So there's still a requirement in this census across the UK at the moment for non-binary people to identify their sex as male or female. And likewise, when it came to the counting of queer identities, and challenges around how that would work, the methods used, we've seen the most high-profile debates around how we conceptualise and make sense of gender and sex, and how trans people are counted in the census. So for me, it became kind of, I guess, a double edged sword was that it’s great that actually, lesbian gay, bisexual people were being counted. This was a development. This was a change from 10/20 years ago. But at the same time, this moment of celebration was also pushing some parts of the wider LGBTQ community further than the shadows. And for me that caused concern because on one hand, I've seen many descriptions of this census being the most inclusive, a real milestone for LGBTQ inclusion, a real kind of high watermark, and it's one of the few senses in the world to ask these questions. But at the same time, those who are arguably most minoritized within the LGBTQ umbrella, are arguably pushed further into the shadows. And this really builds on Dean Spade’s work around deserving and undeserving communities and how actually the advances and the perceived developments or achievements of some parts often most privileged in minority groups, so whether it's white cis, gay men and women and how actually the advances can actually push some of those in the broader umbrella further into the shadows make it harder to achieve meaningful, comprehensive inclusion. And the reason being is if when I kind of critique the census or challenge things around the counting of LGBTQ people I might come across as being a killjoy, being contrarian, being somebody who's just always looking at the negatives, rather than flagging up the fact that actually, this counting these decisions, which ultimately weren't made by LGBTQ communities themselves, ultimately makes the task of counting everyone a little bit harder. So I think there's a few strands there. And there's a lot of actually learning from the work of critical race theorists about the weaponization of the inclusion of some groups can ultimately make the broader struggle for social justice harder. So I think there's a lot we can be learning around LGBTQ data from that wider history of scholarship and activism around racial justice as well.


ELEANOR DRAGE:

That's so interesting. And this is, you know, the key thing is how can we get people to also be interested in this beyond wanting just to do a kind of tick box, Okay, we've included these communities, even if, as you said, it's really not meaningful inclusion, because you're saying, okay, sure, you can be non binary, I'm pro non binary people. But you're saying you can't be non binary in the context of the census. So where can you and can't you be non binary? And it's really indicative of how society accepts you also.


KEVIN GUYAN:

Yeah, and I think it's just this kind of, I guess, hopefully, we're seeing more and more critiques of mainstream liberal approaches to equality, diversity inclusion, it's very much in the past 5/10 years, it's an industry across the UK, and most workplaces have equality and diversity leagues, I've worked in the space for the past five years, with amazing practitioners on the ground doing the work around equality and diversity. But increasingly, when we lose that kind of radical strand of when actually, the very point of doing this work was intended to do, as you're seeing, it often becomes a box ticking exercise, or something that maybe gives us a facade of being inclusive, diverse and kind of advancing equality, but actually when we scratch the surface, we see that we aren't necessarily helping those who are most in need of help. And it's maybe only presenting this kind of window-dressing of diversity and inclusion, that is only helping the most privileged across minority groups


ELEANOR DRAGE:

Well, on that brilliant note of bureaucracy being where radical strands go to die, we will end it here! Thank you so much. It was such a pleasure to have you on the show.


KEVIN GUYAN:

Thank you both.


ELEANOR DRAGE:

This episode was made possible thanks to our previous funder, Christina Gaw, and our current funder Mercator Stiftung, a private and independent foundation promoting science, education and international understanding. It was written and produced by Dr Eleanor Drage and Dr Kerry Mackereth, and edited by Laura Samulionyte.



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