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Episode #142: Catherine D’Ignazio and Lauren Klein
147 Plays6 years ago
On this week’s episode of the PolicyViz Podcast, I’m excited to welcome Catherine D’Ignazio and Lauren Klein, authors of the new book, Data Feminsim. The book is currently open for public review and comment, so you can head over to the...
The post Episode #142: Catherine D’Ignazio and Lauren Klein appeared first on PolicyViz.
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Transcript
Introduction to 'Data Feminism'
00:00:11
Speaker
Welcome back to the Policy Vis podcast. I'm your host, John Schwabisch. And on this week's episode, I chat with Catherine Diagnasio and Lauren Klein, authors of the new book, Data Feminism. The book is actually out for review right now, community review. So you can go to the website, you can read the book and you can provide your thoughts and your suggestions and your comments. And I've put a link to the community review site on the show notes. So I encourage you to listen to the
00:00:38
Speaker
interview and then go over to the website and take a look at the book and see what you think and provide them with your thoughts and your reflections. Before we get into the interview, just a couple of notes.
Support and Workshops
00:00:50
Speaker
This show is completely supported by listeners, so if you would like to support the show to help me buy better audio equipment, better editing services, more transcription services, please consider going over to my Patreon page
00:01:04
Speaker
and supporting the show. You can support as little as a dollar a month, three dollars a month, or even more. I really would appreciate your support so I can keep the show going. I have one more episode coming up for the end of the year and then we'll take a couple weeks off to rest and relax.
00:01:23
Speaker
Couple other announcements. I have a couple of workshops coming up in January. I'll be teaming up again with Stephanie Posovic to put on our data design workshop. We'll be in Amsterdam this time in mid-January. I'll put the link up to that workshop as well. So you might want to check that out. Stephanie and I have been doing a few of these data design workshops where I come from the data side of data visualization. She comes from the design side of data visualization.
00:01:50
Speaker
and we try to take a balanced approach to teaching introductory data visualization. And then at the end of January in DC I'll be teaming up with my friend Brittany Fong to teach an intro data visualization and tableau workshop here in DC. I'll be teaching intro data visualization and Brittany will then be teaching intro tableau. So if you are interested
00:02:14
Speaker
In those skills, I encourage you to check out the website and go over to the registration pages for both of those workshops.
Interview with Authors
00:02:23
Speaker
So this week's episode, as I mentioned, is an interview with Catherine D'Ignazio and Lauren Klein, who are the authors of the new book, Data Feminism. The comment period for the draft is going to close on January 7th. So fortunately I was able to get this interview in under the wire so that you can have your holiday break, if you're lucky to get one, to go over to the website and check out the book and maybe provide your comments and your thoughts and your perspectives on this important topic.
00:02:53
Speaker
So, over to the interview. All right, so welcome to the show. Lauren, Catherine, very nice to have you both on the show. Happy holidays to you. Happy holidays. We're getting towards the end of the year here. It's finally starting to get cold in D.C. So, I think we're all up and down the East Coast here, right? So, Catherine, you're up north. That's right. I'm in Boston.
00:03:20
Speaker
Right. And Lauren, you're in Georgia. I am. I'm in Atlanta. So it's still warm. So we're jealous. I went on a bike ride this morning, although I did wear my warmer bike outfit.
00:03:33
Speaker
I will not be going on a bike ride right now. I was at tapestry in Miami a couple weeks ago and went out in the morning for a run and it was it was like you know low 50s which is cool for Florida and I was in shorts and a t-shirt and kind of feeling like well maybe it's a little too cold and then I saw a guy with a winter hat and gloves and a big jacket and I was like you know what I'm just gonna play the northern card right now and I'll be okay.
00:04:01
Speaker
Well, thanks for coming
Authors' Backgrounds
00:04:03
Speaker
on the show. You have this really exciting book project, Data Feminism, that we'll dive into and talk about. And also, I guess, solicit people to come and give comments on the book, which is great. Why don't we start by having each of you introduce yourselves so folks know where you're coming from. Catherine, you want to talk a little bit about your background?
00:04:21
Speaker
Sure. So hi, my name is Catherine D'Ignacio. I'm currently an assistant professor of data visualization and also civic media at Emerson College, which is in Boston. And my background is as both an artist and designer, as well as a software developer. So I come to data with kind of those two hats.
00:04:45
Speaker
as well as of course being an educator as well. So yeah, and I guess I should mention too, one of the interesting things about kind of the context that I'm in, which actually has led to a lot of this work around data is that I'm teaching data visualization in the context of a journalism department at Emerson College here. So I'm teaching students how to work with data who are non-technical, mostly arts and communications focused. So really looking at
00:05:12
Speaker
data journalism as a kind of interesting intersection of these things.
00:05:18
Speaker
So Lauren. Yeah, hi. So yeah, my name is Lauren Klein. I am an associate professor in the School of Literature. I know it's still new. In the School of Literature, Media and Communication at Georgia Tech. So we colloquially refer to this as Humanities Island. So we teach most of the humanities courses to undergrads and grad students at Georgia Tech who primarily pursue engineering degrees.
00:05:44
Speaker
And my background, I am trained actually as a scholar of early American literature. But even before that, I was also a web developer. Catherine and I have actually realized that we might have known each other in the late 90s, early 2000s when we were both sort of involved in
00:05:59
Speaker
the first dot com wave. But anyway, but sort of during that crash, I went back to grad school in English because I thought you could always rely on books. And then sort of all around me, this new field called digital humanities started to take shape. And this is just a field that's looking at how computational methods and questions about computation more generally can be applied to humanistic research questions. And then sort of the flip side, too, right? How humanity scholars can ask questions about
00:06:29
Speaker
computation and technology more generally. We can probably talk a little bit more about that in relation to this book later.
Collaboration Origins
00:06:36
Speaker
Yeah. So how did you two end up teaming up together for this particular project?
00:06:42
Speaker
Yeah, so it was really interesting. So this is Catherine. And I wrote this blog post, I think it was in late 2015. And I was headed, actually, to the event where John, you and I met, which was the responsible data form event on data visualization. And I wrote a blog post in advance of that to coincide with the launch. And the post was called, What Would Feminist Data Visualization Look Like?
00:07:08
Speaker
And the post ended up going all over the internet. And one of my good friends, Patsy Beaudoin, who at that time I think was a librarian still at MIT, she was like, oh, you have to meet this person, Lauren Klein. She came through Boston and did this amazing talk on feminist data visualization. I don't know when that was, Lauren, but it wasn't that far and long ago in the past.
00:07:32
Speaker
And um, and then did she connect us or did I? Well, you know and then we realized that we were going to meet each other anyway because you were already scheduled to come to this workshop that I was organizing or co-organizing. Okay. Oh, yeah about
00:07:47
Speaker
what data visualization looks like for humanity's data and humanity's research or something. I don't remember the exact chronology, but that was basically it. It was like, Catherine did this amazing blog post and then Patsy connected us, right? Because she had come to my talk. That's right. That's exactly how it happened.
00:08:04
Speaker
Yeah, and then we met at that event that Lauren organized and realized that we had lots of really shared and common interests and both like and also complementary backgrounds and skills and that's what led us we co-wrote first a paper together called feminist data visualization and then realized there was probably enough to talk about but it warranted a book and so that's sort of where the the book came from.
00:08:29
Speaker
Okay, so now you have this book, and it's also open, which we'll talk about in a little bit, but I'm just going to ask, like, what, what is the book? And we'll just, we'll just start there and give people a sense of what you mean or think of when you use that phrase, data feminism.
Feminist Approach to Data Science
00:08:46
Speaker
Sure. So the book, I mean, at its most basic level, it's a book about feminism and data science, right? So it's what can data science learn from the past several decades of feminist activism and critical thought. And we sort of came to this idea because all of the
00:09:03
Speaker
sort of language and use of around data that has to do with power, right? Like we hear people saying data is the new oil, you see how corporations are really, you know, mining data from consumers and citizens for tremendous profit. And you understand how data actually is in this incredibly powerful tool and substrate and sort of vehicle for all sorts of things.
00:09:30
Speaker
And it turns out that actually feminism has a lot to do with power too, right? You know, you kind of assume when you hear the word feminism that it might just only sort of be about women or only for women. But really, feminism, it's about challenging power inequalities and inequities wherever they exist. So I don't know, Katherine, do you want to talk a little bit more about that?
00:09:51
Speaker
Yeah, so we've been doing kind of like, let's get on the same page about feminism, whenever we talk about this book. Because the word has a lot of baggage and people bring wildly different perceptions and histories, so what their version of feminism is. So we like to start with what our version of feminism is. And so the way that we talk about it, thinking about what is feminism, well, first of all, it's a belief. So it's a belief in the equality of all of the sexes and genders.
00:10:18
Speaker
It's also organized activity. So it's like activism on behalf of working towards that equality because we don't live in that equality right now. You just have to look at any data visualization about sex and gender differences in different sectors, let's say, to see that that world is not equal. We haven't achieved that yet. So it's belief, it's activity on behalf of that belief. And then feminism is also this long tradition of theory and thinking about how we can achieve that kind of
00:10:48
Speaker
equality. And so here, there's so many rich and diverse contributions over the past. Really what we're looking at is like probably the past 40 years or so, but just very different fields. And so we looked at feminist contributions and fields like geography, the humanities, science and technology studies, human-computer interaction, design, and so on. But I think one thing that's really important to understand
00:11:16
Speaker
in particular about contemporary feminism is this word intersectionality. So what we're talking about is intersectional feminism. And this was an insight that was formally codified by Kimberly Crenshaw in the late 80s, but also came out of work by the Combahee River Collective, even as far back as the 70s, where their insight is that, like, if we're talking about inequality in the world, we can't just talk about
00:11:44
Speaker
Gender inequality, right because gender inequality is just really inextricably linked with all these other things like class inequality or like race inequality Or like ability or like so many other of these different systems of oppression So that's why when you read it the book you'll see that not all the examples are about women not all the examples are about gender and
00:12:06
Speaker
But like Lawrence said at the beginning, the examples are all about power. And so a lot of what we tried to do with the book is try to bring a kind of deeper and more nuanced analysis of power to the conversation about data and data visualization. Can you give us an example of a power imbalance in data? I think a lot of people who work with data or data science or data visualization, they have some data.
00:12:33
Speaker
and then they go work with it. They present a table or a graph and they sort of, you know, yeah, there are people back there behind who answer the questions, but like I have the data and I'm just going to go do something. So can you maybe give an example of a power imbalance in a data set?
Case Studies and Emotional Impact
00:12:50
Speaker
Sure. Yeah, yeah, totally.
00:12:53
Speaker
So one of the things that we talk about and we try to, we try to bring the conversation a little bit, I would say, lift it out of the data set a little bit to start to consider, well, like, what in the world produces a data set?
00:13:08
Speaker
and looking at the ways that the production of a data set involves different kinds of power struggles. Let me be really concrete about this. One of the interesting examples that we talk about in the book is this example of femicides in Mexico. So femicides are gender-based killings. This is not a situation that's unique to Mexico. This is basically what
00:13:31
Speaker
You might call it both intimate partner violence, domestic violence, as well as other reasons why women are killed simply for the fact of being women. And in Mexico, there has been rising awareness of this problem as there's been rising awareness in other places of these just systemic patterns of violence against women. And yet there is no data, even though this has been
00:13:57
Speaker
called out by Amnesty International, by the UN, even an international court has told the Mexican government that this is a problem in the context of Mexico. There have been folks working on this in the country of Mexico over the past 20 and more years, but there is still no comprehensive data. And so it's this environment which actually produces a lack of a data set, if that makes sense. And this is, I think,
00:14:25
Speaker
pretty illustrative of what happens a lot in relationship to women, but also to people of color, also to folks who are otherwise disenfranchised. So it's like they're they're underrepresented in data because right now we collect data about those things that we care about, right? And there are other things that we care about comparatively less that we don't collect data about. So for example,
00:14:49
Speaker
ProPublica has been doing this recent really excellent reporting on maternal mortality and one of the things they uncovered is that we actually don't have good solid data on maternal mortality statistics in this country and this is something you would think we would this is stuff we should we should know right we should be collecting this is important information and yet the environment
00:15:11
Speaker
has not prioritized it and has prioritized other things. And so the interesting thing we talk about in relation to femicides is that there is this one woman activist who goes by a pseudonym of Princessa and she has determined so that she is going to collect every femicide that she can get her hands on and she actually logs them from media reports and from crowdsourcing every single day. And she's been doing this now for
00:15:40
Speaker
I think about three or four years. And so she has this map. So if you Google Mexico femicides map, you'll definitely turn it up. And so she actually now it's this one individual citizen has the most comprehensive data that is open at least on this phenomenon of femicides. But it shouldn't have to be like that, right? Like there shouldn't be this like one lone person that is like opening herself up to basically safety issues just to collect a data set that
00:16:08
Speaker
folks should be taking responsibility for in the larger institutions. But maybe I'll pass it to Lauren, because she probably has an example as well. Sure, yeah. I mean, there's so many examples, right? I mean, I'm trying to think about, you know, John, you had also asked about not just data sets, but data visualizations, right, that sort of come under this umbrella of data feminism. And some of the examples that I really like to give
00:16:35
Speaker
are ones that you might not think of necessarily as being feminist or sort of able to be understood in terms of feminism because they don't have to do with women, right? So issues of, you know, femicides and maternal mortality, right? Like those involve women and their bodies, right? But ideas about feminism really carry over to, you know, everything that we do, right? So an example, this isn't a dataset, but it's a visualization.
00:17:04
Speaker
You know, the election gauge from the 2016 election, actually, Catherine and I were just talking about that they just brought it back for the midterms, you know, that the New York Times featured that wobbled sort of like a speedometer. I'm sure everyone knows, but I mean, most people listening to this podcast are intimately familiar with this because it was like the most reviled visualization of 2016.
00:17:25
Speaker
People hated this visualization. They said it was manipulative, that it was like ethically, that it was immoral. I mean, people really responded hard negatively to this visualization because it made them feel something, right?
00:17:41
Speaker
And we tend not to think of visualizations as things that should make us feel things, right? But it turns out that people are really bad at interpreting more of the typologies that we use when conveying uncertainty, like error bars or any number of other things, gradients. And there's a researcher, Jessica Holman, actually, who's done a lot of user tests and
00:18:04
Speaker
writing on this, like people when they look at visualizations that are designed to convey uncertain outcomes, they just misinterpret them very badly. This one actually made people feel the way that the uncertainty was playing out, right? And then it made people feel really uncomfortable about that.
00:18:21
Speaker
So Catherine and I like to point to this example as an instance of, it's a really, it's a feminist visualization because it uses a different way of knowing, right? It doesn't use necessarily like objective information or direct visual sort of direct correspondence between sort of
00:18:42
Speaker
the data and then what is seen in terms of some sort of like static graphic or something that is perceived to be, you know, just sort of like a neutral method of conveying what the data really has to say. It actually evokes emotion, right? But the only reason why we feel that emotional knowledge is somehow less good than other forms of knowledge like statistical knowledge or factual, you know, evidence, factual evidence or something like that,
00:19:07
Speaker
is because we have this really, really old and entrenched sort of hierarchy of forms of knowledge, right? And this is a gendered hierarchy, right? You know, you can see this in everything from, you know, again, I'm trying to think of an example of women like the way that medical knowledge, if you've gone to medical school, is valued more than sort of like a
00:19:28
Speaker
a home remedy, right? Something that's learned or experienced through the home, right? We tend to think like, oh, school is better. You know, the way you see, you know, this sort of trained like a chef who's gone to culinary school sort of as perceived or sort of deemed to have more knowledge or different kinds of knowledge and sort of like an intuitive
00:19:45
Speaker
home cook, things like this. But this also breaks down on gender lines as does sort of the more general sense that scientific or objective knowledge is somehow better than knowledge that you obtain through your senses. And this, again, this, you know, it has a really, really long history. And when you take a feminist approach to it, you can ask yourself, well, like, why should a visualization not make me feel something?
00:20:09
Speaker
Like if it conveys uncertainty and it conveys it really well and does so in a way actually that made me feel sort of uncertain to my core, like isn't that a really good visualization, right? Not a really bad one. And so these are sort of the range of examples that we try to talk about in our book. So help me understand the link between the emotional characteristic
00:20:30
Speaker
the draw of that piece and then the power imbalance that Catherine was talking about earlier. So how do those two sort of come together in this example or in any example where the concept of uncertainty elates that there's this emotional reaction because we're kind of, most people are, I think many people at least are probably generally uncomfortable with this idea of uncertainty. We sort of want an answer, right? It's 5% chance, right? So how does that relate to this power imbalance that you're talking about in the book?
00:20:59
Speaker
That's such a good question.
Certainty vs. Uncertainty in Data
00:21:01
Speaker
I think that, okay, so here's another example that actually is more, a little bit more relevant to some of the examples that Catherine was talking about before, but I'm gonna bring it back to the election gauge at the end. So, people like answers, right? People like concrete information in the world, but the reality is that the world is complex and confusing and rarely resolves to certainty, right? Even all of these, these are,
00:21:28
Speaker
especially the election data, it's all probabilistic. It's all running these simulations upon simulations. And so even the data itself is data that is hypothetical. The election hasn't happened yet. We don't really know what is happening. And yet, to display it in a way that it is certain,
00:21:49
Speaker
sort of is trying to either deliberately or unintentionally sort of capitalize on the power that concrete sort of that data has right that data as concrete evidence or you know there are a couple of people who have tried to theorize like how data is different from evidence or is different from fact
00:22:07
Speaker
And what people say is people sort of invoke data or employ data when they sort of want to establish a stable basis on which future arguments can be made. They sort of want something that itself can't be challenged. But the example of the election data is really good because it's not like you actually collected it from people, right? You didn't like take someone's temperature and say, oh, you have a fever because I put the thermometer in your mouth and the thermometer says 103 have to stay home from school or whatever.
00:22:37
Speaker
They ran these simulations. So, you know, already the data itself, it's interesting and it can tell you something, but the data in itself is not sort of objective or sort of tied to, you know, sort of a fact in the world in a direct way. And then when you have it presented in a way that sort of conveys facticity, that's sort of where problems arise, right? And this is something that Donna Haraway, the feminist philosopher of science has been saying since the 80s, right?
00:23:04
Speaker
She talks about something called the view from nowhere. And this is actually a really foundational idea from our book, in which we apply. It comes from Donna Haraway, and we apply it here and broadly. There is always a perspective that is conveyed when you're visualizing data, or even as some of these theorists would say, when you're presenting information as data. It's just that some of those perspectives are not made visible or not acknowledged.
00:23:33
Speaker
And being aware of what those perspectives are, who is creating the visualization, where the data came from. If you're more open about that, then people can actually work towards a better sense of the meaning of the data that they're being presented with. I feel like, Catherine, you might have a better way to say this. Yeah, totally.
00:23:54
Speaker
Yeah, and what I'll mention about the Donna Haraway view from nowhere is that the kind of sociological update version of that for right now is this recent paper that Helen Kennedy and the team of researchers out of the UK did called the work that visualization conventions do.
00:24:12
Speaker
And it's a great paper and it's about an empirical study of people's like lay people's perceptions of visualization. And one of the things that they showed is how the sort of clean geometric forms, straight lines, clean lines, the sort of facticity and the
00:24:34
Speaker
concreteness with which state of visualizations communicate, these kind of conventions of the form work to present this idea of sort of completeness and totality and sort of they make these like truth claims.
00:24:51
Speaker
So I think part of what Lauren's talking about with that gauge that was so interesting is that of course the visualization kind of like flipped on itself. Like it's using some of those similar conventions but then it was like wobbly and it like it destabilized our conventional like
00:25:09
Speaker
assumptions about visualization that it's just true. You know what I mean? Visualization often is like, this thing in the world is true and here's the observation of it or whatever. And the gauge was kind of like, oh, now I'm destabilized and it's given me this emotional reaction to that, you know? And so like, it was kind of almost like a hack of our own perception of visualization, you know? So yeah, that would be my addition.
00:25:34
Speaker
Yeah, it's really interesting. I want to ask you about the book itself.
Public Engagement and Feedback
00:25:41
Speaker
The interesting thing about the book, I think, is that you've opened it up for public comment and I'm curious what that experience has been like so far and also
00:25:50
Speaker
why you decided to do that. I mean, it seems like a lot of people sort of say, I'm writing a book, you know, stay tuned 24 months later, and my book's out. But you're taking sort of a different path with a topic, I would guess, that people probably have pretty strong feelings about.
00:26:08
Speaker
Yeah, I mean, we had a couple of reasons for wanting to open it up and definitely the slow pace of publishing is one of them. And I think I do think it's important to acknowledge for people who have never written a book, there's a lot it's not like nothing is happening. It definitely takes two years to publish a book. But it's not like nothing is happening during that time, right? It goes, you know, it
00:26:27
Speaker
goes out for peer review, it gets reviewed by the editorial board of the press, it gets copy edited, it gets typeset, you know, there's a goes through legal teams to make sure that all the image permissions are, you know, okay, there's actually there's tons of work that happens. And actually, as I've done this a couple of times, I feel like it's important to tell people about that. Because usually you just I mean, I thought this to you, like set your book into the void. And it comes out later as a book book.
00:26:50
Speaker
But we put it online primarily because, you know, we do have a lot of audiences that we want to speak to, right? Catherine and I, you know, we, our interests are overlapping, but they're also, as Catherine said earlier, complimentary, right? So I come at this from a humanities perspective. I wanted to speak especially to students and also to humanities scholars who were interested in sort of looking for examples of how what they knew could apply to data science. Catherine, do you want to talk a little
00:27:20
Speaker
bit more about sort of who you were hoping to speak to. Yeah so a lot of my other work has been about data literacy and so but data literacy for a very specific purpose not just because in general it's good for
00:27:35
Speaker
people to know about data. I mean, I do think it is. But one of the things that comes when we have, you know, as we've characterized, like data has a lot of power that is both economic power and political power and social power. People are talking about even using this metaphor of the fourth industrial revolution to mean this changing methods of data and artificial intelligence.
00:27:59
Speaker
And one of the things that's appeared to me is that not all voices are at the table for whatever this revolution is. We're not all there. And this is a revolution that's being led primarily by folks in engineering and technology, which is not bad. I mean, I'm one of those folks.
00:28:18
Speaker
I really feel strongly we need other voices at the table. So that's why I do a lot of work on data literacy specifically for what I would characterize as public information professionals. So folks like journalists, librarians, municipal government, nonprofit sector, community-based organizations, and all of that to think about stimulating more robust public dialogue about what this shared future of data and technology should look like.
00:28:47
Speaker
That's sort of what brings me to doing this work, is thinking about how do we bring more folks to the table. But then with the open review process, I definitely appreciate that we can get something out there in one year versus two years. This is my first book that I've ever written. It was like, oh my gosh, I write something in one year and it doesn't come out for two and a half other years.
00:29:13
Speaker
But then also I feel like it's very values aligned for us. So one of our core principles of data feminism is around pluralism. And so what that means is multiple voices in the process and running a more participatory process.
00:29:30
Speaker
And so this, to me, seems to really enact those principles by saying, here, it's open. We want your comments. And then we are not just doing it because we want your eyeballs. We're actually going to be reading all the comments. And then they're being going to be used as an input into our revision process in the spring. So I think for that reason as well. And already, people have
00:29:54
Speaker
been extremely generous, like folks that are strangers who we don't even know have given some very long and detailed and really generous comments that are like, oh yeah, we didn't think about that, or we should elaborate on that example more. This thing is not clear. And so it's really gratifying to see that kind of pluralism.
00:30:14
Speaker
Yeah, I mean, I think like we don't we, you know, we're just two people, right? And we have our individual perspectives, you know, we're both academics, you know, we come from our, you know, environments that we live in, and, you know, we can't know everything. And so, and this is true, I mean, of anything, right? Like no single person knows everything.
00:30:33
Speaker
But especially when working with data, which is usually or can easily be sort of marshaled to make these sweeping claims. I think it's a really important process for us to do and also for people to see, to say like the goal is actually better knowledge and more knowledge and more complete knowledge, right? So like, and we really think that we can use data in order to get there. But in order to do so, you need to acknowledge also the limits of what any single person or any single perspective can bring.
00:31:03
Speaker
Right. Try to identify your own or at least acknowledge your own biases and then let people tell you where you're wrong. Yes. So how do people provide comments to you? What's the process like? Yeah, they just go to the website, which I'll say now is a little funny. I'll put it up in the show notes.
00:31:29
Speaker
Yeah, so just you go to the website that John's gonna post and You when you read the draft of the text you just highlight some text. It's very similar to medium.com So you just highlight the text and post a comment that way And you can I think you might need to I think you need to register first and the link to register I think is in the top right. I'm not sure. Okay, and how long how long are you keeping comments open till January 7th?
00:31:56
Speaker
Okay. So people have their holiday break to just sit down for a couple of days. They'll start with the conclusion. Start with the conclusion. Yeah, read it backwards. Okay. So then January 7th, then you go back to editing and then we expect the book and our hands went. The spring 2020 sounds forever.
00:32:17
Speaker
spring 2020 so another another year so everybody has to hold their breath but i've i've noticed that people are excited about about the fact that there is a book on this topic and i know katherine you've been around uh talking about uh this topic did a great talk at info plus um back in the fall and i'll look for that talk uh as well
00:32:39
Speaker
Well, that's great. Thank you both for coming on the show. This is the last podcast episode of 2018. I think it's a great topic to end on, especially this year. Thanks for coming on the show. Thank you. Thank you for having us. Thank you.
00:32:54
Speaker
And thanks to everyone for tuning in to this week's episode. I hope you will go check out the website for the Data Feminism book. Put your comments in, take a look, read, maybe even read some of the later chapters. As I'm guessing, there's probably a drop off. People get in and edit, then it drops off. And so do take a look and provide your thoughts and your comments to get to this concept of pluralism. See, I'm learning all sorts of new things today. It's great, it's great.
00:33:19
Speaker
So yeah, so thanks everyone for tuning in. Have a very happy and safe holidays and a happy new year. And this has been the policy of this podcast. Thanks so much for listening.