CFP: Teemanumero Informaatiotutkimus-lehteen: Yhteiskuntatieteiden ja informaatioteknologian rajapinnoilla (3/2021)

https://www.flickr.com/photos/nickharris1/8026290210
Photo: (cc) Nick Harris

Tämä on artikkelukutsu teemanumeroon “Yhteiskuntatieteiden ja informaatioteknologian rajapinnoilla”. Yhteistyössä Rajapinta-yhdistyksen kanssa toimitettava Informaatiotutkimus-lehden erikoisnumero kartoittaa ja pohtii digitaalisen ihmis- ja yhteiskuntatieteen nykytilaa ja tulevaisuutta erityisesti muuttuvien aineistojen luomien haasteiden ja mahdollisuuksien näkökulmasta.

Informaatioteknologian kehitys ja yhteiskunnan digitalisoituminen ovat kannustaneet tutkijoita eri tieteenaloilta tarkastelemaan erilaisten sosioteknisten järjestelmien ja digitalisoituvien käytäntöjen merkitystä yhteiskunnassa. Esimerkiksi informaatiotutkimuksen, sosiologian, politiikan tutkimuksen ja taloustieteiden aloilla on herännyt kiinnostusta teknologian tuottamista vaikutuksista ja sen avaamista mahdollisuuksista liittyen oman tieteenalan keskeisiin ilmiöihin: teknologiavälitteisyys muun muassa määrittää sosiaalista ja kaupallista kanssakäymistä, poliittista keskustelua ja kansalaisuuden rakentumista, sekä muokkaa yhteiskunnan instituutioita kouluista mediaan. Samalla digitalisoituvan yhteiskunnan kehityskaari muokkaa yhä vahvemmin ihmis- ja yhteiskuntatieteiden tutkimuskohteita, aineistoja ja menetelmiä. 

Sosiaalisen toiminnan jättämät digitaaliset jäljet mahdollistavat uudenlaisten kysymysten esittämisen. Ensinnäkin ne kannustavat tutkimaan teknologian roolia erilaisissa yhteiskunnallisissa prosesseissa. Toisekseen, ne tuottavat tutkijoille yhteiskunnallisen toiminnan aidossa kontekstissa syntyneitä, laajoja aineistoja ihmisten, yhteisöjen ja yhteiskunnan toiminnasta. Samalla ne kuitenkin aiheuttavat uusia haasteita tutkimukselle: miten menetelmällisesti lähestyä näitä aineistoja niin, että yhteiskuntatieteellinen ote säilyy? Miten tuottaa ymmärrystä teknologiavälitteisistä, digitaalisista yhteiskunnan ilmiöistä ilman, että päätyy tutkimaan pelkästään teknologiaa tai pelkästään yhteiskuntaa? 

Tällainen monitieteiseen tutkimukseen ja teknologian ja yhteiskunnan rajapinnalla toimimiseen liittyvä ongelmatiikka sekä tutkimuksen uudenlaiset käytännönläheiset ja episteemiset kysymykset ovat tämän erikoisnumeron ydinsisältöä. Yhteistyössä Rajapinta-yhdistyksen kanssa toimitettava Informaatiotutkimus-lehden erikoisnumero kartoittaa ja pohtii digitaalisen ihmis- ja yhteiskuntatieteen nykytilaa ja tulevaisuutta erityisesti muuttuvien aineistojen luomien haasteiden ja mahdollisuuksien näkökulmasta. 

Rajapinnoilla uraauurtavaa työtä tehneet tutkijat ovat esimerkiksi kehittäneet uudenlaisia, digitaalisen yhteiskunnan tutkimukseen sovellettuja menetelmiä. Näitä ovat esimerkiksi digitaaliset tai diginatiivit menetelmät (Rogers 2013; Marres & Gerlitz 2016; Gerlitz & Rieder 2018), laskennallisen yhteiskuntatieteen yleistyminen (Lazer ym. 2009; Bail 2014; Nelimarkka & Laaksonen 2018), tai verkkoetnografian erilaiset muodot (esim. Hine 2000; Isomäki ym. 2013; Knox & Nafus 2018). Tutkijoita kannustetaankin yhdistämään rohkeasti erilaisia menetelmiä (Laaksonen ym. 2017; Geiger & Ribes 2011) ja kehittämään monitieteistä vuoropuhelua (Halford & Savage 2017; Moats & Seaver 2019). 

Yhteiskuntatieteen digitalisoituminen on herättänyt myös kriittistä keskustelua. Tutkijat ovat pyrkineet arvioimaan isojen digitaalisten aineistojen aikaansaamia muutoksia yhteiskuntatieteelliseen tutkimukseen (Kitchin 2014; boyd & Crawford 2012; Elish & boyd 2018; Frade 2016; Marres & Weltevrede 2013; Ruppert ym. 2013). Yksi merkittävä keskustelunaihe on digitaalisten aineistojen edustavuus ja suhde tutkittavaan ilmiöön: Millä tavoin teknologiset alustat muokkaavat dataa, jota ne ihmistoiminnasta keräävät ja tuottavat? Mitä datasta jää puuttumaan (esim. Hargittai 2020; Tromble 2019; Halford ym. 2018)? Myös digitaalisten aineistojen käytön etiikasta käydään vilkasta keskustelua (esim. Kosonen ym. 2018, Lazer ym. 2020; Zimmer & Kinder-Kurlanda 2017). Kehittyvä tutkimuskenttä kaipaakin paitsi menetelmäkehitystä, myös sen reflektointia, sekä pyrkimyksiä hyödyntää perinteisiä yhteiskuntatieteen menetelmien uuden kontekstin äärellä.

Ehdotettavat tekstit voivat olla teoreettisia tai empiirisiä artikkeleita, katsauksia, kirjallisuusesittelyitä ja -arviointeja, tai ne voivat pohjautua hyväksyttyihin opinnäytteisiin. Soveltuvia tieteenaloja ovat muun muassa yhteiskuntatieteet, humanistiset tieteet, ja kasvatustieteet sekä näiden alojen kysymyksiä tarkastelevat muiden tieteenalojen tutkimukset.

Ehdotukset voivat käsitellä esimerkiksi seuraavanlaisia aiheita:

  • Tieteiden ja konventioiden rajapinnoilla: monitieteinen työskentely yhteiskuntatieteellisten kysymysten äärellä
  • Reflektioita käytettyjen työkalujen vaikutuksesta tutkimuksen suunnitteluun, rahoituksen hakuun ja käytännön toteutukseen, tutkimuksen tuloksiin ja päätelmiin sekä eri tieteenalojen kehityskulkuihin
  • Digitaalisiin aineistoihin ja laskennallisiin menetelmiin liittyvät käsitykset, toiveet ja epäluulot
  • Laskennallisten menetelmien ja digitaalisten aineistojen soveltaminen yhteiskuntatieteissä: tapaustutkimukset ja kirjallisuuskatsaukset eri tutkimusaloilta
  • Uudenlaiset menetelmät yhteiskuntatieteissä: esim. koneoppimiseen perustuva aihemallinnus, verkostoanalyysi, lingvistiikan menetelmät 
  • Uudenlaiset menetelmien ja tutkimusotteiden yhdistelmät: esim. makro- ja mikro-näkökulmat, subjektiivinen vs. objektiivinen tarkastelu, laadullisten ja määrällisten menetelmien yhdistäminen.

Tietoa Informaatiotutkimus-lehdestä: https://journal.fi/inf/about

Erikoisnumeron vierailevat toimittajat: Salla-Maaria Laaksonen, Thomas Olsson ja Jesse Haapoja. 

Abstraktit pyydetään toimitettavaksi 7.1.2021 mennessä. Toimittajat ilmoittavat hyväksymisestä viimeistään 31.1.2021 ja käsikirjoitusten eräpäivä on 30.4.2021. Hyväksyttyjen käsikirjoitusten työstämiseksi järjestetään maaliskuussa online-workshop. Abstraktit sekä kysymykset ja yhteydenotot teemanumeroon liittyen pyydetään lähettämään osoitteeseen teemanumero@rajapinta.co.

Viitteet:

Bail, C. A. (2014). The cultural environment: Measuring culture with big data. Theory and Society, 43(3), 465–524. https://doi.org/10.1007/s11186-014-9216-5
boyd, d & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society 15(5), 662–679.
Elish, M. C., & Boyd, D. (2018). Situating methods in the magic of Big Data and AI. Communication Monographs, 85(1), 57–80. https://doi.org/10.1080/03637751.2017.1375130
Frade, C. (2016). Social Theory and the Politics of Big Data and Method. Sociology, 50(5), 863–877. https://doi.org/10.1177/0038038515614186
Geiger, R. S., & Ribes, D. (2011). Trace ethnography: Following coordination through documentary practices. Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/HICSS.2011.455
Gerlitz, C., & Rieder, B. (2018). Tweets Are Not Created Equal: Investigating Twitter’s Client Ecosystem. International Journal of Communication, 12, 528–547. Retrieved from http://ijoc.org/index.php/ijoc/article/viewFile/5974/2252
Halford, S., & Savage, M. (2017). Speaking Sociologically with Big Data: Symphonic Social Science and the Future for Big Data Research. Sociology, 003803851769863. https://doi.org/10.1177/0038038517698639
Halford, S., Weal, M., Tinati, R., Carr, L., & Pope, C. (2018). Understanding the production and circulation of social media data: Towards methodological principles and praxis. New Media and Society, 20(9), 3341–3358. https://doi.org/10.1177/1461444817748953
Hargittai, E. (2020). Potential Biases in Big Data: Omitted Voices on Social Media. Social Science Computer Review, 38(1), 10–24. https://doi.org/10.1177/0894439318788322
Hine, C. (2000). Virtual ethnography. London: Sage. https://doi.org/10.4135/9780857020277
Isomäki, H., Lappi, T.-R., & Silvennoinen, J. (2013). Verkon etnografinen tutkimus. In S.-M. Laaksonen, J. Matikainen, & M. Tikka (Eds.), Otteita verkosta. Verkon ja sosiaalisen median tutkimusmenetelmät. Tampere: Vastapaino.
Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 2053951714528481. https://doi.org/10.1177/2053951714528481
Knox, H., & Nafus, D. (2018). Ethnography for a data-saturated world. Manchester, UK: Manchester University Press. https://doi.org/10.7765/9781526127600
Kosonen, M., Rydenfelt, H., Laaksonen, S.-M., & Terkamo-moisio, A. (2018). Sosiaalinen media ja tutkijan etiikka. Media & Viestintä, 41(1). https://journal.fi/mediaviestinta/article/view/69924
Laaksonen, S. M., Nelimarkka, M., Tuokko, M., Marttila, M., Kekkonen, A., & Villi, M. (2017). Working the fields of big data: Using big-data-augmented online ethnography to study candidate–candidate interaction at election time. Journal of Information Technology and Politics, 14(2), 110–131. https://doi.org/10.1080/19331681.2016.1266981
Lazer, D., Pentland A., Adamic L, ym. (2009). Life in the network: the coming age of computational social science. Science 323:5915, 721–723.
Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., … Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1126/science.aaz8170
Marres, N., & Weltevrede, E. (2013). SCRAPING THE SOCIAL? Issues in live social research. Journal of Cultural Economy, 6(3), 313–335. https://doi.org/10.1080/17530350.2013.772070
Moats, D., & Seaver, N. (2019). “You Social Scientists Love Mind Games”: Experimenting in the “divide” between data science and critical algorithm studies. Big Data & Society, 6(1), 205395171983340. https://doi.org/10.1177/2053951719833404
Ruppert, E., Law, J., & Savage, M. (2013). Reassembling Social Science Methods: The Challenge of Digital Devices. Theory, Culture & Society, 30(4), 22–46. https://doi.org/10.1177/0263276413484941
Tromble, R. (2019). In Search of Meaning: Why We Still Don’t Know What Digital Data Represent. Journal of Digital Social Research, 1(1), 17–24. https://doi.org/10.33621/jdsr.v1i1.8
Zimmer, M. & Kinder-Kurlanda, K. (2017). Internet research ethics for the social age : new cases and challenges. Sage Publications.

Lectio Praecursoria: Imagining the Data Economy

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cc: Loguy

Lectio Praecursoria presented in the public defence of my PhD thesis “Imagining the Data Economy” on April 25th, 2020 at 12 noon, using remote connections via Zoom. The lectio has been lightly edited for online publication. Full PDF version of the PhD thesis is available online: https://www.utupub.fi/handle/10024/149155.

***

Learned Custos in Turku, my esteemed Opponent in Hamburg, ladies and gentlemen in the audience,

Before imagining the data economy, let us start with something from the past. The history of capitalism is a history of transforming things into resources and commodities. Consider the things we call natural resources. Coal was once a rock in the ground, and fisheries were fish in the water. Environmental historians1 remind us that coal and fish did not become resources by themselves. It took specific economic relations and specific cultural conditions for this to happen.

Digital data is also a thing that has become a resource. You can often hear the claim that data is like a natural resource2, but data is always created on purpose. There was never a time when data was like a rock in the ground; just strings of ones and zeros that somehow ended up on the hard drive or the Hadoop cluster. But the role of data as an economic resource has certainly changed. Our digital habitat is now built and run by firms that rely on data about people for the production of services and the creation of economic value.

The productive activities that involve data about people form something that we might call a personal data economy; a system that uses personal data to provide things that meet human needs.

A short while ago, it seemed that citizens will mostly derive agency and empowerment from new technologies that rely on data. With hindsight, it seems somehow naive that we were so optimistic about web 2.0 services, free as the radical new price, and the democratization of information production. We have begun to understand the relations of power that underlie these technologies, and their implications for our lives. We are increasingly aware of the potential that data has for social change, for surveillance, for economic exploitation, and for control of others and the self.

If things are transformed into resources and do not simply become resources, how was data turned into the resource it is today? How did we get to the data economy we now live with? 

The underlying technological change came with information technology. Already in the 1980’s, Shoshana Zuboff3 pointed out that information technology does not just automate, but it also informates, or produces information about what it automates. Today, a more common concept for this is datafication4. We use technologies to communicate, but those technologies also produce data about us communicating. We use technologies to search for things, but the same technologies also find us. Technologies help us travel and transfer things efficiently, but also make these movements visible and available for others.

Datafication technologies make the production and exchange of data possible. They are a necessary condition for today’s data economy. But an economy does not emerge from technology. Since Max Weber, sociological views of the economy have underlined that economies consist also of the social, cultural and institutional context in which production and exchange take place. This means that to take a sociological view of the data economy, we must pay attention to the socio-cultural and institutional context of data production and its economic use.

There is extensive literature on this context of the data economy, but it is not always framed as such. Surveillance studies, data studies and data privacy research have examined the imperatives driving the production of data, the arrangements that go into that production, and their implications for people as data subjects, citizens and consumers. 

Taken as an analysis of the data economy5, this literature reveals a distinct logic that underpins how datafication is used to produce things that meet human needs. Competition drives firms to collect more and more data about people’s lives. Simultaneously, people have become accustomed, or even resigned, to commercial surveillance practices. The markets in which exchanges of data happen are often business-to-business. These markets are not accessible for you and I. Also the formal institution of data protection seems to act as a sort of partner-in-crime6 here: it makes people responsible for their data, but data is not an easy object of individual calculations of costs or value. 

While we benefit in many ways from things provided with our data, we lack the means to make data serve our own ends on our own terms.

Could the data economy work in a different way? 

When conditions change, resources might turn into something else again. Consider coal and fish. Coal might turn from a resource into a lump of condensed carbon emission. Fish might become an unethical source of protein or an endangered species. Nothing inherent in coal or fish needs to change, if the socio-cultural and economic context changes.

Data could also be turned into a different resource, and the data economy could work in a different way. Many ideas about a different data economy are out there, and today you don’t even have to look very hard. 

Not long ago, a government minister in Finland launched a programme related to personal data by mobilising a Finnish cultural trope. He asked, “Can Moomin Valley challenge Silicon Valley?” You might be excited or disappointed to hear that this Moomin Valley of personal data is about data governance. But when the data economy only exists in a socio-cultural and institutional context, a different model for data governance is also a vision about how the data economy should work. Moomin Valley’s data economy is supposedly different from the one they run from Silicon Valley.

In the thesis, I examine an idea for data governance – and therefore, an idea for the data economy – that has been developed in the context of data activism. Here, I follow Stefania Milan and colleagues7, who suggest data activism as a heuristic device. This means I use data activism as a tool to examine engagement and political action that take a critical stance in relation to data and their distribution. 

My empirical case is MyData. It is based on a vision of a more just digital environment that is achieved when people have the right and the practical capability to manage their data. Framed in terms of the data economy, MyData envisions an alternative economic order, in which data is not just a resource for companies, but also something that people themselves decide about. When people manage data about themselves, it is thought to become available for novel ways to produce value.

The original research in the thesis was published in four articles that examine data activism from different standpoints. Two of the articles focus on end-user technologies that aim to realise the practical capability to manage data. The other two focus on investigating MyData as an emerging form of data activism.

To draw together this research, I adopt and adapt the theoretical concept of collective imagination.

I use the concept in two senses. The first one follows Charles Taylor8, who discusses collective imagination as that which helps people to make sense of the society around them. Taylor talks about social imaginaries, or widely shared understandings that are taken for granted and that have achieved general legitimacy. To put in short, the social imaginary concerns how things work in the world and what is normal. Following this line of thought, I consider the current social, cultural and institutional context of datafication as the dominant imaginary about the data economy.

The second sense in which I employ collective imagination is to examine alternatives to this dominant imaginary. According to Sheila Jasanoff9, collective imagination produces socio-technical imaginaries, or shared visions of a desirable future which are achievable by means of technoscientific advancement. Socio-technical imaginaries have a distinct forward-looking element to them. Sometimes they are counter-imaginaries to dominant ones; in these cases, they are visions of avoiding an undesirable future. Following this idea, I look at data activism as a source of alternative imaginaries about the data economy. A further insight gained with this theoretical lens is that there can be many concurrent imaginaries about the data economy within data activism, or alternatives within the alternative. They can embrace contested values and can work towards different – and divergent – futures. Examining these alternatives makes it possible to do research on different potential data economies that are contained within data activism.

There are three main research questions in the thesis. I will discuss them briefly one by one. The first research question concerns how alternative imaginaries developed in data activism compare with the dominant imaginary.

The issue at stake here is data agency. By this, I refer to the capacity to act intentionally in relation to personal data. In the thesis, I describe different forms of imagined data agency. The bottom line is that they concern economic participation. In the alternative data economy, people are imagined as agentic participants in managing their lives in situations that involve the production and use of personal data.

Data agency is imagined not only to turn people into economic participants, but also to make data move. When people control data, it is imagined to become available for businesses that currently lack access to it. This means that two different goals are combined here: empowerment of people in datafied times, along with new opportunities to do business with data.

The second research question concerns how different alternative imaginaries developed in data activism compare with each other. The answer is that while data agency is a shared goal, there are different notions about what data agency means. These different notions serve as entry points into different imaginaries about the data economy. I call these the market imaginary and the citizen imaginary. 

In the market imaginary, data agency is the capability to make choices on the collection, sharing, and use of personal data. People examine market offerings and use data to improve their lives, making personal data serve their own ends. Data governance happens by market forces, and efficient data markets guarantee desirable societal outcomes. The value expected from data is related to the improvement and efficiency of personalization, use experience and service provision. As I argue in the thesis, this imaginary does not so much question dominant economic rationales, but rather transforms them to serve the ends of data activism.  

The citizen imaginary, in contrast, views reliance on the market as dubious. In the citizen imaginary, data agency is rather the collective capability to participate in processes that determine how data is used. Data is viewed as a collective rather than individual resource, and outcomes for people collectively are brought to the foreground. This relies on explicit models for collective governance of the collective data resource. As I suggest in the thesis, this imaginary contains possibilities for novel ways of making data valuable, which are rooted in the good of the collective that produces and sustains the data resource.

The third research question of the thesis is, how can we identify and promote desirable imaginaries about the data economy?

Given the critical research on datafication, it seems justified that a researcher does not only observe attempts to shape a different data economy, but also participates in this shaping. But remaining committed to a critical position means that also the agendas and problem settings of data activism require critical attention. The four published articles that form the core of the thesis reflect different ways to engage with data activism in a manner that has been indicated by Bruno Latour: doing critique that does not only debunk, but also assembles. Throughout the research, my aim has been to produce knowledge that is relevant in a social scientific sense, and can also be relevant for data activism practice.

The answers to the question about desirability are far from conclusive. This is, after all, about imaginaries and potential outcomes. As I describe in the thesis, the market imaginary seems likely to expand to agendas beyond data activism. But reliance on individual market agency appears as a precarious effort to shape a desirable data economy. Given the nature of data as a resource and given the power relations in the data economy, the citizen imaginary seems like a more promising point of departure. 

What I suggest in the thesis as a desirable data economy imaginary is something like a synthesis. It builds on the notions of collective data governance and new, collective ways to make our data valuable. At the same time, to take hold of the broader collective imagination, it should attach onto things that already provide economic and social value with data. Collective forms of governance can regulate the use of our data as an economic resource, but this does not mean precluding the production of economic value. 

An imaginary about the data economy is not a recipe for building services or a model that can be implemented. It is rather a way of looking at the world and the relations between people and data, and the production of things that meet people’s needs. 

But different imaginaries lead to different material things, different technologies. Different technologies can have different societal outcomes. 

For data activism, the implication of this research would be to move beyond the individual as the locus of data control. Unlike us social scientists, data activists have the capability of putting together experimental data technologies and governance structures. It is important to experiment with different – and diverse – ways of forming resources based on data, different ways governing these resources, and different ways of relating and attaching these resources to the data economy that is already there.

How we collectively imagine makes a difference. Imaginaries matter and have consequences for the present and the future. 

Data technologies will continue to have implications for our lives, whether we want it or not. Their development is shaped by imaginaries about how the data economy functions and should function. Social-scientific scholarship has a role to play in shaping the data economy. It can identify and kindle those imaginaries that shape pathways towards a desirable digital environment, a data future that we would rather live in.

Professor Ingrid Schneider, I respectfully ask you, as the Opponent duly appointed by the Faculty of Social Sciences for my disputation, to present any criticisms you may have against my doctoral dissertation.

***

1 – Such as Jason Moore. For this particular argument, see for example: https://jasonwmoore.wordpress.com/2013/07/04/anthropocene-capitalocene-the-myth-of-industrialization-ii/ 

2 – For an elegant discussion on data vis-à-vis natural resources, see Kelly Pendergrast’s essay “The Next Big Cheap” in Real Life Magazine https://reallifemag.com/the-next-big-cheap/

3 – See Zuboff, S. (1985). Automate / informate: The two faces of intelligent technology. Organizational Dynamics, 14(2), 5–18. http://dx.doi.org/10.1016/0090-2616(85)90033-6 

4 – “Datafication” was introduced into social-scientific lingo by José van Dijck: van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208. https://doi.org/10.24908/ss.v12i2.4776 

5 – This deserves more references than can be listed here. See Chapter 3 of my PhD thesis for a more thorough discussion on the social, cultural and institutional context of data production and data use: https://www.utupub.fi/handle/10024/149155

6 – This felicitous phrasing was first used by Sami Coll: Coll, S. (2014). Power, knowledge, and the subjects of privacy: Understanding privacy as the ally of surveillance. Information, Communication & Society, 17(10), 1250–1263. https://doi.org/10.1080/1369118X.2014.918636

7 – For discussion on data activism as a heuristic, see Milan, S., & van der Velden, L. (2016). The alternative epistemologies of data activism. Digital Culture & Society, 2(2), 57–74. https://doi.org/10.14361/dcs-2016-0205 

8 – See Taylor, C. (2002). Modern social imaginaries. Public Culture, 14(1), 91–124. https://doi.org/10.1215/08992363-14-1-91. For an extended discussion, see Taylor’s 2004 book with the same name. For an accessible overview and discussion on related literature, see Ruppert, E. (2018). Sociotechnical Imaginaries of Different Data Futures. An Experiment in Citizen Data. Erasmus University Rotterdam. https://www.eur.nl/sites/corporate/files/2018-06/3e%20van%20doornlezing%20evelyn%20ruppert.pdf 

9 – For an STS take on the topic of collective imagination, see Jasanoff, S. (2015a). Future imperfect: Science, technology and the imaginations of modernity. In: Jasanoff, S., & Kim, S-H. (Eds.) Dreamscapes of Modernity. Sociotechnical Imaginaries and the Fabrication of Power, 1–33. Chicago, IL: University of Chicago Press.