2020_SEPT_24 WRITING MEETING_Transcript

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Angela Okune 0:24
This is the core group, we may have one or two more people slip on. ... So this call, for context, is our second call after we decided that we're gonna write together and that we decided we're going to focus on the dissertation chapter. And that will hopefully turn into the ESTS article on infrastructures next year. So it's the third kind of of the special collections that we'll be working on. And so we're hoping to generate a lot of materials, basically focused on our working group. And I think we talked about kind of an autoethnographic approach of basically reflecting on why we came together our own unique experiences being part of the group. And Wangari was just saying, it feels overwhelming. I think that's okay, we can talk about that in, you know, what I mean, like just reflecting on all of all of it all. And, and so we've kind of just gotten off to start, and we're still figuring out how we're going to do all this. But the plan is to use the research data share site essay as kind of our workspace and then I think we'll probably co write together in the Google Doc that I shared with everyone. (Everyone else turned off their videos, why? We can turn off her videos if you want for for bandwidth, you guys, but it's totally fine to keep your video on.) Um, so what was I saying? Yes. So working together and in RDS and then writing together probably in some sort of Google Doc, we haven't figured out the writing part yet. But right now, I think what we're planning on doing is working through the data that we already have generated. And then annotating that together, and then going from there and seeing what we want to say. But anyway, so so the first thing I also shared, were a couple of readings. And these readings Kim actually shared with me, and I hadn't read them. So I started reading them this week. And, and they were pretty dense. And so I realized it might be nice to have Kim also join us on the call. Because her work has has really played off and made use of a lot of these concepts and others. And I thought it would be nice to have her help us frame the readings a little bit. I don't know how much you guys have been able to read all of them yet. They're quite different. Yeah, they use the concepts in different fields and with different kinds of domains and applications. So I was hoping that you can maybe start us off and then feel free to drop off if you want, you know, you don't have to stay for the whole call. And then the rest of the group, we can kind of dive more into also logistics towards the end and figure out kind of what we want to do next, etc, etc.

Kim Fortun 4:15
So why don't one of you tell me a little more about the kind of question you're trying to address in your writing about infrastructure? I mean, is it "How can infrastructure support Open Data practices and open research in the Nairobi context?" Is that the question?

Angela Okune 4:39
So I think it's around there. Um, I and maybe this is also on me so so for -- and Kim, you, you know, this--but for the dissertation chapter, part of why I'm calling it deutero and why I kind of threw those readings out there. Hawi, Wangari, and Wambui were probably like oh okay, what's this Deutero thing because we've always talked about this chapter as kind of like just reflecting on us as a working group. But for me, the deutero chapter, which is the last chapter in the dissertation, really is about thinking about data capacity in Nairobi and thinking about what kinds of skills, research and otherwise, we need and want to see moving forward. And so and what thinking about data and working on data allows us to do beyond just tech and data. So like, what kinds of collaborative infrastructures, community building infrastructures, technology infrastructures, yes, but the skills that also need to come about hand in hand with that, and how we see that...I don't want to say filling a gap...but how that is different from what's currently going on, or how that, yeah, dovetails off of what we currently see, in terms of the discourse and the work on data going on in Nairobi.

Kim Fortun 6:02
So I think it'll be helpful to know just a little bit about Gregory Bateson, the figure that these concepts that Angela has put forward comes from, especially the concept of deutero. Bateson is famous for this concept of deutero-learning. And Bateson was a really quite crazy in the best sense of the word, anthropologist, British, was married to the anthropologist Margaret Mead, and did field work with her in Bali, but also did field work in New Guinea. And notable thing, like in Bali, one of the things he and Mead did is use the camera for the first time in ethnography and took like a zillion pictures so that they could characterize particularly mom-child interactions, but very experimental, like, didn't start knowing we're going to do this with the camera, but using the camera as a new tool to kind of think in new ways. He has a famous book called Naven, about a puberty ritual in New Guinea. But what's interesting, I think, for us about the book, is it's organized, where he tells the story of the ritual three times through three different analytic lenses. One a psychological lens, one a social, I'm forgetting the third. And so he was interested in how we see things and how we learn to see things in new ways. And that's really core to do deutero-learning. How does the system not just repeat itself, but actually learn, "learn to learn" that's what deutero learning is. And I think it's interesting for this project, because part of the promise of new data practices, new data infrastructure, is that not just we'll add more data and more information and more knowledge, but we will learn to produce new forms of knowledge and new practices that can really attend to changing conditions. So it's really a way of building capacity, not to address a stable ideal, like modernization, but capacity to address a continually refreshed capacity to address changing conditions. So it's really exciting as an educational concept, it's very hard because you don't teach...it's not informational...you know, I don't teach Angela, learn these four things and then you'll have deutero capacity, it's much more you create the conditions in which people can learn to do something more than rote reproduction. Deutero learning is put in comparison to proto learning, which is repeat, you learn well, you learn to, you know, if you, you know, and he connects the biological and cognitive so like bodily muscle memory is something that you cultivate for proto learning. And importantly, you don't skip over proto learning. So becoming very skilled at something is a precondition to the kind of expertise that's actually more flexible. And so one, and it was, it was a good challenge for me to think about how the concept can maybe be useful for your group. And Angela has shared a little bit of your exchange, but the possibility of open data, enabling a kind of knowledge production practice that is both localized and yet not isolated out of a comparative and transnational perspective. So I think one thing I want to talk y'all through a couple of questions, and then I think then I think we can get to deutero questions. But when you think about more open data data infrastructure in the Nairobi research context. What do you worry about, like, You worry that if you want to, you're going to worry that it's become the same old thing. Tell me a little bit about what would be a disappointing result, 10 years from now with open data.

Angela Okune 10:31
Well if others are thinking, I can step in, because open data has been almost 10 years. So the official open data, which is more statistical and government open data, was launched in 2011. So it is actually about 10 years. And they're actually they reached out to Leo, who's not on this call, but the other day to ask her to help them revise the policy, because I think they've been disappointed with how it's been executed, and pretty much because it's not used. So I think that's what's disappointing about open data, in its current form, and with its current data sets in Nairobi right now is that it's not being used.

Kim Fortun 11:11
Why isn't it used? meaning it's encouraged and just people aren't taking it up, or it's legally mandated, or just ignoring it, or...

Angela Okune 11:21
The datasets themselves are not being updated and refreshed, and so they're stale. Many of them are incomplete. I think there are questions about the quality of the data itself that's in the portal. And then since a lot of the the Portal was funded by the World Bank, for example, and other donors. And I think it's been funded by like three cycles, and every time the cycle kind of dries up, then support for it, and the champions kind of also move on to the next thing. And so it doesn't have an organic, sustained community drive behind it. It's often donor-driven.

Wambui Wamunyu 12:06
I was just going to say, I think part of the problem is that there is no buy in at, you know, the people who matter, you know, if it is a government department, for example, that should be uploading information on our website, as part of open data principles. And by the way, we have, you know, a bunch of new laws that are encouraging public participation and citizen access to information and all that good stuff. But it's, it's all on paper, but it relies on will for it to happen. It relies on political will, and you know, different types of will even just the interest to do it. So if you lack that, and then you lack an accountability system, that will kind of push it, where I think even for us as citizens, we need to be much more engaged. I think that's where the problem is. So I think I agree with Angela, that a concern would be that we have these wonderful laws and policies on paper that say, you know, information should be available and accessible to all. But we still do not kind of come out to make sure that the information is current and is accessible, and that people know that it is there. So I think that would be a disappointment. If further down the road, we don't do much with open data. And maybe one more thing I'd say concerning data or information is that we have this flexible approach where you say, yes, we can get budget information, for example. But beyond budget information, then let's connect budget information to what are we buying for our hospitals? What are you paying for our schools? And you know, and beyond that, you know, it kind of has a ripple effect. My fear is that there will be no ripple effect. We'll be stuck at, let's get the budget information. And we don't see the bigger picture.

Kim Fortun 14:21
That those are really good articulations and I have a few comments. Do you want to add anything to the list of what you're worried about? [silence] Okay, let me put a question back to you. It sounds as though one reason there's not investment in providing the data is that people aren't enrolled in the promise of the data. In your mind, is the connection to public participation? So it's not just people making the data available, but people wanting it there so that they can use it. Is one way to put the goal of open data, collaborative knowledge production? Where citizens can have have the same data that government officials have so that there can be critical analysis of decisions about what to buy in schools. Is collaborative knowledge production--and thus accountability--is that a good way to put how you see the promise of open data?

Wangari Ngugi 15:39
Maybe I could comment, I was excited when you said the word people because I'm a psychologist and I was thinking, do people actually want collective data? I must say it happens, but more informally. And my experience, just getting data from NGOs, nongovernmental organizations, or CBOs, community based organizations is, it's like we're doing all these things, but we don't really think we should be writing it or recording all these things or transcribing or archiving. So that's an issue about the value and the priority as Wambui said, of this data. So what? So what if we get all this data and archive it? I mean, it's happening informally, we are always doing collective knowledge production, that's for sure, that's straight from our traditional systems to date, you know, that's going on. But the more formal systems, why I was really attracted to this space was also the qualitative inquiry bit, which, you know, I'm a qualitative research, and I think qualitative methods have really been downplayed by, especially at high level academia, you know, but I was happy for a space where I could actually generate qualitative data amongst peers and create it live in a system, you know, co-create it. I would say that we actually need as a priority to be data driven, we are not a knowledge economy yet, in my opinion, we are, you know, just mostly waiting for whoever donor will come with anything without really defining, more formally, our needs. I think we do have people doing collective knowledge production, but just not formal and just not archived and just not, you know, packaged. So it will be more like, random, than more systemized. That's my take. And especially the mindset around research has also been heavily driven with quantitative, you know, methods and not really valuing that there's this other whole world and we could actually contribute to it.

Kim Fortun 18:01
Thank you. Um, so this is really useful too. The point that there's already a lot of collaborative knowledge production, but it sounds like it's the kind of collaborative knowledge production within a very, like within these donor-driven projects, where you know in advance what you're producing, it's not really exploratory. Is that fair to say?

Wangari Ngugi 18:28
Yeah, that's right on the money. That's right on point, yeah. So not really from the grassroots up, it's just whichever begging bowl, we can bring, and whatever we can catch, not really also creating our agenda and saying, this is really where we would like more funding in. For instance, my work that is very highly specialized is with a deaf community. And it's very typical that I will not get data when I need. And so it has been a big frustration, even at an individual level. So I'm glad for this space to collaborate and create, you know, a system and I'm also interested on learning about the systems, you know, that that system of knowledge production, and how that would run. So this research is on many levels, very useful to me. It's not just the actual work we are doing, but how we are doing it is part of the research. As you know, in qualitative methodology, the researcher's role is very crucial. So it's very exciting for me.

Kim Fortun 19:34
Now, one thing I hear...Yes, go ahead.

Hawi Rapudo 19:39
Okay, I think, thank you very much for this discussion, one is, the definition of what you're talking about is not very clear. Why I'm saying this is because we're dealing with different scope of people in Nairobi. We're not only dealing with one scope. We are dealing with illiterate and literate, we are dealing with rich and poor. We are dealing with modern and we're dealing with traditionalists, we're dealing with pagans, we're dealing with Christians and Muslims and different people who have different faiths. So the data we want to pick out, the funnel on the layers of what you're talking about, it's not very clear in terms of who consumes what data. Why I'm saying this is you go to universities, you find that a lot of data which are generated every day through a lot of research. And through a lot of things that are happening and nobody's out there, because you want to pass exams, you go to different schools, and even in different places. We have been capturing in images and cultural issues, which are barely defined. And like some of the songs that I've been seing sung recently by young people within the cities of Nairobi, you can't even understand what they are saying. But that's a new language called sheng, where they're twisting a lot of words and taking it backwards. And so you know, that's what we're trying to like this kind of new meaning. What is it so that this data is ...and it's quite a lot?... and so that's one thing. Also, we need to look at who wants this data? It looks like everybody wants this data, but they're not aware. That's the other ...what do they buy from the data we are talking about? So for me, those are very critical issues, like who buys what? Everybody outside there wants data but they don't tell you. Even the person selling maize might need data, which is...So those are the kind of things...I'm just trying to, because I'm a sociologist and that's why I look at things from a sociologists' perspective, given that with a bias of governance and security, which, which is my my field that I work around. So while I'm sitting here, I look at those kind of like, how do you make the city better in the next coming 20 years and these people, interaction and this clash, the clash of civilization.

Kim Fortun 22:35
So this is this is an example I think of the kind of problem characterization that you're seeing as needed through new data infrastructure. And if I hear you right, your emphasis is on the need to characterize, understand and deal with a lot of difference, difference across generation, difference across class, religion, etc. And so you need to...do you think the phenomenon ... the heterogeneity of Nairobi...Is that poorly understood? Or just, it's the next step that's the problem? Like how do you deal with it so that it's a productive rather than disabling such differences?

Hawi Rapudo 23:30
It's something which is not understood. And that's why you find that one, we are struggling with our civil servants who do not know the meaning of data, which are one of the ...I just talked about, even updating the website and making sure that their website has information that can target young people who are in their 20s, and are looking for work, what kind of language ...someone was dropped out of school, someone or someone was being sexually molested and wants some information on how to deal with certain situations...they can't get this information because maybe that have not gone to school, and the kind of language maybe they use to understand each other is also different, ... because this is the generation we're talking about this is the bigger layer, this is the bigger layer. And that's, that's the layer that needs a lot of information ...And then there's also another issue in terms of who do we serve this information to? Because one of the things is that you say every day, people collect a lot of information I've been to every hub in Nairobi and you see a lot of people coming up software applications, coming up and at the end of the day, they fail. Because they don't test it, they don't involve the public. So there's a disconnection between all these kind of players.

Kim Fortun 25:10
So I think one thing you're getting to which I heard also in in the point that, you know, the concern that people would get stuck on one kind of data and not what i what i hear you saying is ... there needs to be data creativity, like ... really think about what kind of data you need to characterize a problem like this, if the data isn't available, because, you know, you're trying to understand kids that aren't in school, and thus, you can't capture them with school data. You need people to be creative about what substitute data, or proxy data that you could use in order to understand things that you don't have data for. And this is a big problem in my area of research, I mostly work on environmental issues. On one hand, there's a lot of data. On the other hand, there's a real scarcity of data. So I think the best practitioners are very creative about, you know, if you're missing data, can you use this data instead, knowing its limits, so you have to be able to be very reflective about the limits of data, because a lot of the data in the environmental world I expect, like the social data in Nairobi, it's very noisy, you know, it's incomplete. You don't you don't know where it was collected or who it was collected. And I think this does relate to the Deutero capacity where you want to not only build the data, but you want to build the capacity to use that data in creative ways. But it's not a linear process, because unless you enroll people in the promise of what you could do with data, they're not going to share the data, they're not going to do the extra work of making it available. And I also just heard in these comments, something Angela has described for me before is, you know, it's not for lack of research in Nairobi. There's a lot of data collection going on. But it tends to be very...asking the same questions over and over again. So it is, you know, there's a great example from Bateson of, you know, he actually tested some of his research on animals and dolphins. In one of the articles you read, there's this really memorable story of how you can train a dolphin to do its tricks. But what happens when the dolphin learns that what it gets rewarded for is doing a new kind of trick. And so the dolphin learns what the context is, and thus can do something new. And so I think that really what it would look like to build a creative Data Commons in Nairobi, is you have to get people not thinking in these very narrow terms about what good data is, I think the project based, donor model is a big part of the problem that I've heard a lot about in Latin America, too, because you're told in advance what you're supposed to get out of the data collection, there's no time to, you know, kind of push things to the next kind of step. Tell me more about like, when you're training a new generation of researchers, I mean, your students and the people that will come after you, what are the things that will keep them from becoming creative data practitioners, and you mentioned exams, the way they're trained to, like go from, you know, they're trained to use their research to meet these either pass an exam or satisfy the donor, but what are the things that are kind of pressing down on people so that they can't actually be creative in their data practices? So funding? An exam based system in education, what are the other things?

Wangari Ngugi 29:37
Um, I would say there are two major challenges if I would classify them, systemic challenges and individual challenges. I'm trying to remember the person that said this quote that in the 21st century, the biggest task of any learner will be to learn and learn and to relearn. So its a very famous quote now that we're using in the 21st century. So I would say on an individual level, those people with that character strength of being flexible, or resilient, or expedient, or prompt, or savvy, especially with technology, they will fare better in this century. Because it's not so much what you know, but how you learn, I think when you started off, you talked about learning how to learn. And I think that that's going to be at an individual level, I'm already beginning to see, as a psychologist, I'm interested in, you know, individual differences, I'm already beginning to see people who are struggling with this transition into online learning, you know, and people who are acing it, who are having a good time. There are all kinds of individual differences, and they are complex. On the systemic level, it's basically what we've said. And in addition, I would say as well, status quo, a lot of the professors just holding on to only quantitative and only a certain way of learning, and especially in the research space, where I am most active, there is a hierarchy in knowledge production, who owns knowledge? And, yeah, we know that knowledge is not so democratic, and there are societies who own you know, things. But I think that knowledge should really be for every human being who has a capacity to think, you know, but we all know that that's not the case. And knowledge is very, very warmly embraced by those people who think they should know and other people who should not know. And so there's all these hierarchies, and it's a big barrier to creativity. Another systemic challenge is funding. I'm always in meetings where, you know, people are praised for having gotten all these funding, and I say, what about when I did a grassroots campaign and, and fundraised on my own and got this amount, you know, to do things, you know, being facilitated being incentivized, I think has become the buzzword for research. And, you know, we call it vitamin m, when people want money just to talk even just to say, Hello, you've got to drop a coin first. It's become like a real commercialization of knowledge, which actually impedes free thinking. Thank you.

Kim Fortun 32:41
That's really insightful. And I think we spoke at the beginning about if you want to tell someone "don't treat knowledge hierarchically," even if they agree with you, which is not very often, they don't know how to not do that. Like, it's not so easy as just following instructions, being obedient, like the dolphin being obedient, you have to change something kind of behind cognition that puts them in a different place. And I think this is the promise of infrastructure, where designing the infrastructure, so that you're not telling people to change, but they're working in a different way. And they come out of the infrastructure, with a sense of the possibilities of qualitative data, or a sense of the possibilities of having five people producing knowledge together, where they don't know at the beginning, what the hypothesis is, you know, where it really is exploratory.

Wangari Ngugi 33:45
And I think you're right, the context, changing that context is difficult. And, and another one more thing that just came to my mind as you spoke. A lot of published works are biased from the start, not only in terms of knowing the hypotheses, but that the results should be positive. So, in positivist, you know, models, they kind of want to say, we are not concerning a null hypothesis, this has got to like really produce real significance, even if I would like to do qualitative data production, and I want to do all this, you know, findings, publishing my findings, but who will be my audience? I mean, I live in a, in an infrastructure, in an ecosystem that does not celebrate that, you know, it's a real struggle. And, again, people are looking down on when qualitative research is published. And, you know, and always not being defined on its own terms. It's defined on the terms of quantitative which doesn't even make sense. Those two are beautiful on their own and they should just stand alone. Not necessarily one defined in terms of the other.

Kim Fortun 35:03
Is a pathway to changing this at a broader level, to and you know, as mentioned, civil servants, as important actors was mentioned earlier, like you want them a civil servant responsible for education in schools, for example, to be more open to what they can learn from qualitative data. For example, is that one way to think about one of the goals of this kind of effort?

Hawi Rapudo 35:34
Okay, I think that's a good question. And I'm learning a lot in this process and the process of learning. And it's taking me into deep thinking, because I'm asking myself who are the custodians of information. Where does the information come from? Then once I give out the information, because the citizens are the custodians of information, the citizens are the ones who share information each and every day. In everything that you do, everything that we live, that's where the information whether you go to shop, where they go to birthday party, whether you go to sporting events, because we congregate us, in a sporting events fans, sharing a particular sporting event. And that becomes a process when I go to shop around I go to a place called a market, and this is where information is collected. Because of the sovereignty of the nation, we've given people a mandate to run our governments through the Constitution. And it makes the civil servants very powerful, because through that power we have given, and that information is kept in, in laws called Official Secrets Act, which becomes a very big challenge for and restricts free access to information. So that becomes very, very important, because one we are dealing with data, that that will also become a reference point for us, when I talk about access to information and issues of Official Secrets Act. That is what we are seeing happening even in the Internet, where we have a lot of internet shutdowns because people cannot be able to share information back and forth. So that will also affect...because it has happened in Nairobi, where you find that some parts of Nairobi have not been able to have access to information. Because one is that within the informal settlements, so when you build informations, informal settlements, it means the neighborhood where you're living cannot be able to get Speed Internet, which means are not able to get. So one of the other things is that when you see civil servants are not able to give information within their website, if you go to the offices you'll find they are working on rusty desk, it's because it's a design. It's a designed process, which means it's setting people back in terms of development through a corrupt system. So that becomes very ... when dealing with the issue of data infrastructure. Because now it has become very good because people are able to access information through zoom, I can be able to communicate to the lowest civil servants at the lowest level through Zoom, but they choose what information to give you. So that's where the challenge is, and how do you deal with this kind of, because that's where the paradigms that we need to climb over and deal with this. Because it's a funnel and when you are climbing on different layers. What are the layers? Because the person who controls wealth in this country, in this city, is the person accesses information at every minute or every second... and that's where we are having a lot of challenges.

Kim Fortun 39:38
And part of your critique is that that, so one, the hierarchical access to information but also the person that has access, the civil servant, for example, they're not being creative in their use of inflammation. They are using it in a very instrumental way... is that part of your critique?

Hawi Rapudo 40:00
Yes, yes, because you see what is happening, if you look at the elections of the city, the governor who was elected has been associated with being a member of a gang. And you see gang-ism has now become part of now, the influence around that, because the data we are getting right now is not data, which is based on people's values...data which is based on certain business, enterprise and certain elites who are controlling certain information. You find, even as my colleagues were talking about, like the other week, the county government of Nairobi was saying that they're no longer going to be paying for security for the schools. So the schools are going to pay for the school security by themselves. What does it mean? It means now the school is going to take back the cost to the parents. And it's going to affect the issue of quality of information and quality of education. Because now the parents will be working so hard to make sure the children at school, paying more for COVID, paying more for security, paying more for other things. So it becomes very challenging, because these parents cannot access, do not have time to access information. And would also not have time to educate ... because they will be spending time on looking for... how to take the children to school.

Kim Fortun 41:43
So is one of your points here that if parents had had different data, they would have protested the shift of, you know, the security expense to the schools? And so they would have been more active respondents to that change?

Hawi Rapudo 42:05
Oh, yes. Because one is that, you know, these are growing cities, like Nairobi is a growing city and it's growing every day. So we need like data, which can be like...parents can get information very fast, and see how to do to deal with these kinds of challenges. Because one of the biggest things that also we look at is time, or time is affecting resources, because one is that you are in an office where you're taking maybe four hours, or five hours to get certain information, then either you'd have used that time in business, or at your office space, or doing other things so you find out that time is lost. And if you combine those many hours, you find that you're losing billions and billions of shillings because of lack of investment in data infrastructure.

Kim Fortun 43:04
So one way I hear this, I mean, I think we can take it back to deutero learning is that the current uses of data and the possible uses of more data is that it wouldn't change what people are seeing with the data, because it's so rigidly framed, that you get more data, you don't understand the problem in a new way. Or the many problems. And so the challenge of building infrastructure that that breaks the frame, so to speak, of how people are understanding the city, distribution of wealth, etc. And the idea that you need students not just staying within the frame, but being creative about when the frame needs to change.

Hawi Rapudo 44:04
Okay, sorry, another issue that I also want to say is about the giving up about education, because young people are now also seeing that there's no need of going to school. Because some of them are seeing that they can make wealth very fast through gangism, through prostitution, through cartels. So that is also a notion that is coming up, so like some people are seeing some of their colleagues finished the school and maybe they've gone to university, but they don't get jobs but there's someone who dropped out of school, maybe at the lower primary level. This person has walked out and is now driving big cars, controlling a lot of wealth. And so that's also a notion that is there. And so the issue is about how this lack of information about how people can help their own life skills to survive -- survival skills, especially for those people who are trying to live around their own ways, especially women, like women have to survive because of the issue of a lot of domestic violence and a lot of issues, because women are so much disadvantaged within the city. And they cannot be able, like they're because they're looking after some of them are looking after children. Some of them are trying...abandoned homes, a lot of single mothers, a lot of...so I'm just trying to open up the debate...

Kim Fortun 45:48
So I think one thing I would suggest is, as you all continue your discussion, to keep running this list of problems that new data practices need to address, and social problems like the one here where what I the way I hear it is, you know, we're in the near term need for survival, people are opting for, you know, are seeing, you know, quick wealth generation as the best answer to their problems in a way that actually has a very short lived success. And so you want them to be able to see the bigger consequences or longer to the problem of, you know, civil servants making decisions about how to fund education in a way that, you know, doesn't recognize, or it's not, at least not up for public debate that it actually would undermine participation in the educational system because of how it shifts the burden. So if you have a list of just tough problems that data needs to help you understand and respond to, and then a separate kind of list of what's a bad way to deal with those problems, like what's a bad use of data that would just reproduce the same old way of describing them, that doesn't go anywhere. And like, in my own research, for example, in describing the health effects of pollution, there is a strong emphasis on disparities, poor people get more pollution than rich people. And that's true. But we know that, like we've known that for 30 years, it hasn't moved anything in the way that we've dealt with the problem. So we need to not just characterize problems, but have a characterization that allows you to kind of shift it. And so the kind of things that would be poor outcomes of data practices, and then the third...part of the challenge is you don't know what good data practices should do; you want to leverage the promise of good data. And so how do you change the conditions of knowledge production where people would be more open to the use of qualitative data, or more open to a kind of community based research that really listens to young people, for example, and doesn't just, you know, put them in that same old box. And just an idea to think about is, we have some other researchers in our community that work with the digital platform you're using, that are thinking about what kind of community data infrastructure would be useful in their communities. And so one thing you might do as a kind of thought exercises, is imagine projects that might move the way people think about and use data. And I think akin to the event that Angela, you ran at the library. Like if you brought young people into the research data share infrastructure. How could you design a little project? What I've found is if you bring people into data infrastructure they learn to love data in new ways because... if it's loosely enough structured where it's really exploratory, it gets to be kind of addictive because they want ...you know, they want more data and more data, you know, they begin to see what you can see with it, but you really, you have to be inside of it to know. And an example I'll give you is: years ago now, environmental data, like pollution data became available on the internet. And at first people were very critical. They said, you know, information is not power just because you know, the pollution in your community does not mean that you can fix it that you have any power over the companies and the government. But what happened is, people started exploring that data, and it made them angry. And so the way that it, that data availability, grew up a generation of environmental activists that are just incredibly strong. So a very unanticipated use of that data. Because the purpose was not really it told them how to fix the problem. It produced these environmental activists that you know, previously were not mobilized citizens. One of them, I still work with many years later, she was a shrimper, you know, mom with five kids, not an activist. And she started exploring data and man, she is a force to deal with, now incredibly expert on the companies that are polluting her community. And so imagining what kind of data infrastructure and data availability would actually produce that kind of change in Nairobi, where you have parents mobilized as education activists, or people mobilized for transportation infrastructure, where you actually have a citizenry that's asking something. Has ideas to ask of the civil servants, but you also want the civil servants mobilized in a different way.

Angela Okune 52:06
I see that we are actually running out of time. But this has been so good. Um, I just want to add, maybe one thing is that I think what you've described, Kim, was kind of the orienting ideal that started a lot of the open data movement push. Yes, an active citizenry that will hold the government to account, you know, using information that they now have access to, that used to be behind closed walls, but now people can access and then they get energized, and then they want to hold people accountable. But then that never really played out. And then it became a tired kind of narrative that was then used to get donor money. And then you know, open data for better governance and for transparency and accountability, like those are all key buzzwords, you know, that ended up being just used on both ends from those who apply for funding, and then the funders who give them out, like in their calls for proposals, you know, it just became kind of another part of the next thing that then got funded.

Kim Fortun 53:17
I think that's a double bind, right there - is that it's not that you don't need more transparency and a mobilized citizenry. But even that has been locked into a certain frame. And so, you know, it's become a kind of cynical endeavor. And I think that's precisely I mean, the kind of inertia you get when something that is, in fact, promising becomes kind of locked down. That's a real double bind. And so, you know, it's those kinds of contradictions that you need to creatively imagine what kind of data infrastructure can work within that paradoxical space.

Angela Okune 54:03
That doesn't then also get locked down or captured in this like highly donor funded and corporatized space.

Kim Fortun 54:12
Right. Well, and the potential for commercialization and kind of marketization of anything that comes out of the system.

Angela Okune 54:23
That's my worry. Is that this could also equally be turned into...get captured in that same way and that people will then look to qualitative data, just as the next frontier for the same kind of commercialized model, not for its radical potential.

Wangari Ngugi 54:42
Yeah, I like that you've captured the word paradox, cuz it's a contradiction. Yeah. Yeah. And I'm sorry, I need to leave just this minute and it was a privilege and an honor to be with you all hang out for one hour and really learn a lot. It's been an interesting journey being on this team so I appreciate it.

Angela Okune 55:02
Thanks Wangari, we'll chat more.

Wangari Ngugi 55:21
Thank you.

Angela Okune 55:40
Wambui, did you want to say something?

Wambui Wamunyu 55:46
Yeah, I thought it'd be easier to just take some of my thoughts and reactions so nothing to add right now. But I found the link between some of the concepts, you know, the double bind for deutero learning and post-normal science now, the direct connection to what we are doing...because the readings were, you know, applied to different contexts. And to really see this, "normal science," I think that's what we are trained to do. Even in, you know, as scholars, or, you know, like, if you're doing a master's program, or if you're doing any research, when you type in, you just stay within the lines... count the people, and just figure out what that counting means. And what's interesting for me even in what we are doing, is, when we are saying let's go beyond counting the lines, you're essentially coming up against power structures. And what's actually very, very interesting for me is that it's linking and I tried to say this in one of my earlier comments, it's a dismantling of structures across the board. In Kenya, right now, we are talking about how colonial structures remain in our system. And they've created a lot of inequality, marginalization, oppression, and exploitation and all that stuff. And I connect this to the Black Lives Matter movement in the US where it's essentially about that--marginalization, oppression, exploitation, etc. So when we talk about, and I think I'm just having a very dim light bulb moment at this point, but when we connect this even to our research situation, where we are saying we don't have to do normal science. For us, we are literally coming up against yet another power structure. And it's uncomfortable. And when you come up against a structure, you are causing trouble, so to speak. And trouble means resistance. And resistance comes in different forms. So but I think this is good. You know, yesterday, I was in a, a panel discussion about press freedom. And as we were talking, I just almost got goosebumps thinking that this so called freedoms that we keep talking about are linked: academic freedom, the freedom to ask questions, to talk to anybody, and the person with data is not just a civil servant, or the government minister or the World Bank, it is me. And I have access to the resources to also ask you questions, and to get answers, and that those answers are not thrown in a bookshelf somewhere to gather dust, but I can use them for my good for my community's good and so on. So I think interestingly, this whole discussion is exposing deeper, you know, kind of political problems. And these political problems are manifested, you know, in academia, in media, in, you know, all these sectors, where we now have people fighting for Black Lives Matter. Or, you know, let's break down these colonial systems. Let's bring down these statues of these racist people who colonized us. It's, um, I don't even know I don't even know what I'm thinking right now, but I'm feeling like it's a bigger thing. And it's a good thing, because it is saying ultimately, it is about people. It's about individuals. It's about dignity and human dignity, which has been denied. I have a right to ask questions. But I have a right to also be asked questions and contribute to making where I am better. Because I'm giving you information. We are collaborating, we are not...it's not silos, because that's the other problem we have. We operate in silos, I gather data, and I keep it to myself, and I'll use it for what I want. So the World Bank will gather its own data. And yeah, they will have a repository, etc, etc. But its World Bank data. One of my relatives is in the nutrition field. And she was listening to a panel discussion about, you know, nutrition, in Kenya, etc, etc. So, it was, I think, co-funded this, this panel, or co-hosted by UNICEF. Now, the Food and Agriculture Organization, which does similar work around nutrition will not be at such a panel, because there's politics around it, you know, who will host and so on. Now, what kind of nonsense is that? And we're really just talking about, you know, how can we help people be food secure, eat better, you know, all that good stuff. So I, I'm just fascinated, and I think I'm going on and on, but I'm just excited. But I'm just fascinated by when you go down to the root, whatever sector you are in, research, media, whatever, you name it, even community work. We are ultimately talking about the same thing. Let's just break down this top down power focused setup that ignores the ordinary person who should be at the center of it all. Angela, you asked for it, you told me to speak...

Hawi Rapudo 1:01:59
Wambui, I love the way you're putting it out. That is quite passionate. Something I just need to share is about innovation. I sat down with some of my friends and we were talking about innovation this weekend. And I told my friends, if we go about innovation, the way we are going, we're not going to achieve certain things. There are people who walk around in the market and sell buns, what we call mandazis, and they started this innovation almost around two years ago, where they would come every day in the morning when people are passing. And they will sell two buckets. And in each bucket, we had almost over 100 of them at five shillings which means five shillings and 100 that comes to around 500 shillings. So in a day, in a corner, they're making close to 1000. So this man, or this person, there's a group of people in informal settlements have almost around 100 points that are selling. So this is information when someone is looking at information and looking at information on innovation. Someone looks at it, it has to be a Silicon Valley, or something like that. But these are things which people are doing outside there. There are people even who are selling chapatis for a living. And these chapatis they are selling for a living, they are selling close to almost around 200 of them per day. Better than someone who's going to an office and selling chapati--those are like pancakes-- and someone selling almost close to 5000 of those kinds of chapatis in a day. So, this is information about what is being sold outside there. And this is data within the people which is not put on public domain. One is because people go for conventional information, or we call it conventional wisdom.

Kim Fortun 1:04:40
You know, this kind of example, and associated data is going to become increasingly important in the US context. Because in the COVID situation where established jobs have just disappeared. I think more people are going to be producing income through the informal sector. And so the question of how do you cultivate innovation in the informal sector, you know, think, you know, using examples like to think with and to think about what innovation is, it's going to have a global significance in a way that it didn't a few years ago, because of the kind of informalization of the economy, even in places like the US. So it's interesting to think of Nairobi as a rich site for learning about that, that will have a lot of relevance, you know, beyond Nairobi.

Hawi Rapudo 1:05:45
The other thing also is about publishing. And this is the struggle that we've had every now and again about publishing. How do we publish and how do we go about everything? Because do we go for the conventional way? How are we going to publish data, how are we going to publish information? How do we make sure that everything's happening? Because this is where we get it wrong... Because people want us to publish in journals, people want us to publish in certain... which are authorities. So how do you deal with those? That issue of publishing information?

Kim Fortun 1:06:38
You know, one thing I just want to say that has come to me in this conversation, is that I think part of what you're dealing with here is how do you deal with postcoloniality in knowledge practices and infrastructure? Because there's been a lot of work on how postcoloniality is everywhere, but we're talking about how where its legacies are in knowledge production. And you know, in terms of Bateson, I think postcoloniality is rather like proto learning. It's like the repetitive rote learning, you know, like it keeps keeps reproducing itself. But if you really want to understand like emergent forms of innovation, not only what data do you need, but as you pointed out, how do you design the infrastructure so that it's, it's really generative in that way? I think your group can do a lot of really, really important thinking on this. So it's just great that you all have come together.

Angela Okune 1:07:52
Well, thank you. I know we've lost some folks as everyone drops off. But thank you so much, Kim, it's been really helpful to have you here to kind of keep asking more questions for us to think about.

Kim Fortun 1:08:04
I always have questions.

Angela Okune 1:08:07
And they're always so good.

Kim Fortun 1:08:08
Are you going to transcribe this?

Angela Okune 1:08:13
Yeah, I'll save this. And then I can transcribe it and share it. We have our archive of our calls.

Kim Fortun 1:08:24
You're really making new articulations here. So it's important. It's really great to both do it and, and return to it and analyze these conversations themselves. So I'll come back and talk to you again at some point, I hope.

Angela Okune 1:08:39
Yes, please. Thanks.

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Created Date

September 24, 2020 - 10:00am

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Contributed date

September 28, 2020 - 5:20pm

Critical Commentary

Here is the transcript of the meeting of the writing subgroup of the Research Data KE Working Group held online on September 24, 2020.

The video recording can be found here.

During this call, Prof. Kim Fortun helps frame Bateson's concepts of deutero-learning and double bind which the group had read up on (see readings here) prior to the call. Kim probes the group to think about what deutero learning and capacity might address and seek to disrupt in the Nairobi data context.