Societal Problem Solving and University Research : Science-Society Interaction and Social Impact in the Educational and Social Sciences

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Terhi Esko

Societal Problem Solving and University Research

Science-Society Interaction and Social Impact in the Educational and Social Sciences

To be presented, with the permission of the Faculty of Educational Sciences of the University of Helsinki, for public discussion in Athena building lecture hall 107, Siltavuorenpenger 3 A, on Friday May 15 2020, at 13 o’clock.^th

Helsinki Studies in Education, number 74 Helsinki 2020

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Reviewed by

Postdoctoral Research Fellow Piia Vuolanto, Tampere University Adjunct Professor Mika Nieminen, Tampere University, VTT Custos

Professor Sami Paavola, University of Helsinki Supervisors

Pofessor Emeritus Reijo Miettinen, University of Helsinki Professor Juha Tuunainen, University of Oulu

Opponent

Professor Mathieu Albert, University of Toronto

Yliopistopaino Unigrafia, Helsinki ISBN 978-951-51-5996-0 (paperback) ISBN 978-951-51-5997-7 (pdf)

University of Helsinki, Faculty of Educational Sciences

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Terhi Esko

Societal Problem Solving and University Research

Science-Society Interaction and Social Impact in the Educational and Social Sciences

Abstract

This study contributes to the understanding of social impact of research and its achievement in the educational and social sciences. The aim of the dissertation is to uncover how the interactions between researchers and their surroundings develop and how diverse fields in the educational and social sciences contribute to the society.

In the context of innovation policy, which emerged in the 1990s and 2000s, the university has a central role in knowledge production. In the policy realm this was called the university’s third mission. Universities and researchers are expected to produce added value, innovations, and economic benefits for stakeholders outside the university, such as industry and political decision-makers. In addition to this, research is to contribute to complex societal and political questions. In innovation policy and the research evaluation literature, the focus has been on quantifiable outputs, which tend to favor the natural and technical sciences.

In this dissertation, consisting of four articles, I follow the work and research findings of two research groups with the help of case studies. One of the cases focuses on the educational sciences and research on learning difficulties. The second case is an analysis of multidisciplinary urban studies and the study of social segregation. Both cases represent public good and policy-relevant research.

The empirical data collection took place between 2011 and 2018 consisting of interviews, documentary data and policy guidelines. Several analytical strategies were used to ensure methodological triangulation of the data.

The findings suggest that the social impact of academic research should be understood through its various dimensions: epistemic, artefactual, social- institutional and geographic. These dimensions depend on the context in which research is conducted but also on the stakeholders and beneficiaries that researchers have. In addition, the concepts of context and stakeholder should be analyzed with more nuance and detail in order to understand social impact.

Keywords: social impact, academic research, universities, third mission, innovation, research evaluation, educational sciences, social sciences

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Terhi Esko

Yhteiskunnallinen ongelmanratkaisu ja yliopistotutkimus

Tieteen ja yhteiskunnan vuorovaikutus ja yhteiskunnallinen vaikuttavuus kasvatustieteissä ja sosiaalitieteissä

Tiivistelmä

Väitöskirja käsittelee tutkimuksen yhteiskunnallista vaikuttavuutta kasvatus- ja sosiaalitieteissä. Tavoitteena on ymmärtää yhteiskunnallista vaikuttavuutta sekä niitä vuorovaikutuksen muotoja ja keinoja, joita tutkijoilla on ympäristönsä kanssa.

Innovaatiopolitiikan vahvistumisen myötä 1990–2000 –luvuilla yliopistojen asema niin kansainvälisesti kuin kansallisella tasolla on aiempaa voimakkaammin sidoksissa niiden tuottamaan taloudelliseen hyötyyn. Tutkimuksen halutaan tuottavan lisäarvoa, innovaatioita sekä edistävän talouskasvua. Lisäksi tutkimuksella halutaan ratkaista monitahoisia yhteiskunnallisia ongelmia, jotka vaativat monitieteistä osaamista.

Tutkimuksen arviointikirjallisuudessa sekä innovaatiopolitiikassa pääpaino on ollut määrällisten mittareiden luomisessa. Nämä mittarit soveltuvat huonosti kasvatus- ja sosiaalitieteisiin. Väitöskirjassa yhteiskunnallisen vaikuttavuuden muodostumista seurattiin kahden tapaustutkimuksen kautta. Oppimisvaikeuksien tutkimus sekä monitieteinen kaupunkitutkimus edustavat sekä julkiseen hyvään liittyvää tutkimusta, että niin sanottua politiikkarelevanttia tutkimusta.

Aineistonkeruu tapahtui vuosien 2011 ja 2018 välillä. Aineisto koostui haastatteluista, dokumenttiaineistosta sekä politiikkalinjauksista.

Menetelmällisesti tapaustutkimus hyödynsi erilaisia analyysimetodeja.

Väitöskirjassa kehitettiin tutkimuksen arviointiin soveltuva kehikko, jossa yhteiskunnallista vaikuttavuutta määritellään sen ulottuvuuksien kautta.

Episteeminen ulottuvuus liittyy tutkimusalan, sen teorian ja metodien kehitykseen. Sosiaalis-institutionaalinen ulottuvuus kuvailee tutkijoiden verkostojen laajuutta. Väline-ulottuvuus korostaa välineiden kehityksen merkitystä vaikuttavuuden toteutumisessa, joka pisimmillään näkyy maantieteellisen ulottuvuuden kautta, kun vaikuttavuus laajenee yli maiden rajojen.

Väitöskirjan tulokset korostavat sosiaalisten ja yhteiskunnallisten tavoitteiden merkitystä tutkimuksen kohteiden määrittelyssä. Lisäksi väitöskirjassa kiinnitetään huomiota kontekstin ja sidosryhmän käsitteiden määrittelyyn, jotka ovat avainasemassa yhteiskunnallisesta vaikuttavuudesta puhuttaessa.

Avainsanat: yliopistot, yhteiskunnallinen vaikuttavuus, tutkimus, kolmas tehtävä, tutkimuksen arviointi, kasvatustieteet, sosiaalitieteet

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Acknowledgements

In 1928, when Amelia Earhart embarked on a journey, she was determined to fly across the Atlantic Ocean. On the question of whether she had any doubts, her reply was“When a great adventure’s offered you—you don’t refuse it, that’s all”¹. In 2011, I was offered the opportunity to start a dissertation project when my supervisors Reijo Miettinen and Juha Tuunainen suggested that I continue with the topic of our latest project on the third mission of the universities. A great adventure began without me knowing it. My journey has not involved a trip around the globe - only short “expeditions” to Europe. Nevertheless, it has been an adventure – once you start, you really do not want to give up. The exhaustion, the doubts, the endless detours and finally the joy when your paper is published after many review rounds, has been my modern equivalent of a great adventure.

Several people have contributed to my work by providing encouragement and wisdom to see this endeavor through. I want to thank my supervisors Reijo Miettinen and Juha Tuunainen. Your support and guidance have been priceless.

You set the bar high and helped me achieve much more than I ever thought I could.

Thank you.

I wish to thank the CRADLE community that provided me with support and an academic homebase. Thank you Yrjö Engeström, Jaakko Virkkunen, Hannele Kerosuo, Sami Paavola, Annalisa Sannino, Juhana Rantavuori, Anu Kajamaa, Hanna Toiviainen, Tarja Mäki, Jenni Korpela, Jiri Lallimo, Ulla Björklund, Heli Kaatrakoski, Marika Schaupp, Auli Pasanen, Leena Käyhkö, Päivi Ristimäki, Kirsi Kallio, Giuseppe Ritella, Anne Laitinen, Päivikki Lahtinen, and Minna Vasarainen. Throughout the years, the “Cradlers” have been eager to expand their network abroad. This means that there have always been visitors, students and research staff coming and going at CRADLE. Having a vivid network of colleagues has enriched my work but also opened new doors in later life.

A journey is best enjoyed with company. I turn my gaze back to the Developmental Work Research and Adult Education (DWRAE) doctoral school of 2012 that from the very start was a “beehive” of thoughts, discussions and – most importantly – get-togethers over good food. I want to thank these fellow academic trekkers for giving me warm memories that will last a lifetime: Liuba, Monica, Hongda, Martin, Yura and Masha.

The University of Helsinki is my alma mater in many ways. In the past, I had the pleasure of working with colleagues in the Faculty of Social Sciences.

Especially Antti Pelkonen, Karoliina Snell, Tuula Teräväinen and Aaro Tupasela

1 “Preface to greatness” by captain Hilton H. Railey, In: This World, Vol. 2, No. 21 San Francisco Chronicle, September 11, 1938. Retrieved online at The Museum of the City of San Francisco:http://www.sfmuseum.org/hist6/amelia3.html

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have left their mark on my way of appreciating the aspects of academic collaboration. Another colleague, Elina Mäkinen has given me new perspectives and with her, I share a past in sociology.

My work is indebted to the participants of the study: the research groups followed in this work have enabled rich material through which to study the social impact of research in fields that provide Finnish society with important knowledge, tools and perspectives on social matters.

As in all endeavors and expeditions, somebody must sponsor great adventures.

Funding for this dissertation came from a range of sources. Some of the data were collected during 2011–2012 in the“Varieties of the Third Mission: University- Society Interaction in Different Disciplines” project, which was funded by the Network for Higher Education and Innovation Research (HEINE). In addition, The Finnish Multidisciplinary Doctoral Training Network on Educational Sciences (FinEd) has supported my work in the years 2012–2015. I also received funding from the University of Helsinki’s Faculty of Educational Sciences on three occasions for which I am grateful.

Outside the walls of alma mater, there is the world that knows me as something other than an academic. I want to thank my friends Marika, Minna, Johanna I., Sanna, Piritta, Johanna K., Katja, Hanna, Saila, and Liekki. A special thank you to my cousin Katja, who has shown a lot of enthusiasm for my work.

With a big thank you, I dedicate this doctoral dissertation to my parents, Tarja and Juhani Tuominen.

Helsinki, 30.3.2020 Terhi Esko

No borders, just horizons – only freedom.

- Amelia Earhart -

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List of original articles

1. Esko, Terhi, Tuunainen, Juha & Miettinen, Reijo (2012): Social Impact and Forms of Interaction between University Research and Society in Humanities and Social Sciences. International Journal of Contemporary Sociology, Volume 49, No. 1, April 2012.

2. Miettinen, Reijo, Tuunainen, Juha & Esko, Terhi (2015): Epistemological, Artefactual and Interactional-Institutional Foundations of Social Impact of Academic Research.Minerva (2015) 53:257–277.

3. Esko, Terhi & Tuunainen, Juha (2019): Achieving social impact of science:

Analysis of public press debate on urban development and policy. Science &

Public Policy,Volume 46, Issue 3, June 2019, 404–414.

4. Esko, Terhi & Miettinen, Reijo (2019): Scholarly Understanding, mediating artefacts and the social impact of research in the educational sciences. Research Evaluation, Volume 28, Issue 4, October 2019, 295–303

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1 Introduction

Universities contribute to society in several ways. They provide an educated workforce for the labor market and conduct research in a range of fields. This way, they affect their surroundings both directly and indirectly. Besides these two traditional missions, universities have encountered increasing demands from outside to contribute to economic growth and to solve complex social questions such as environmental issues and questions related to political decision-making.

Since the 1980s, universities have encountered restrictions in funding systems, and they are required to be more interactive with other stakeholders. Therefore, their contribution to society has become an additional mission, sometimes characterized as the “third mission” of universities. However, the idea of relevant research is not new:“Academic science is not an isolated enterprise. To keep its position in society requires legitimacy. Practices in science have changed over the course of centuries, but science has always depended on societal support in the form of resources and legitimacy." (Hessels & van Lente 2009, 388)

In terms of public funding, universities and higher education institutions in Western countries have gone through extensive reforms and restructuring emphasizing the engagement of academia with the economy and society (Venditti et al. 2011, Zomer & Benneworth 2011). Attempts to understand and conceptualize the changing science systems and new modes of knowledge production have suggested that the contract between science and society has changed in a profound way (Gibbons et al. 1994, Nowotny et al. 2013, Martin 2003). Therefore, universities find themselves in a new situation in which they need to account for, and make explicit the usefulness or value of, their research efforts to outside stakeholders.

Apart from the idea of a social contract between science and society, universities as organizations have been re-organized and their relationship with the state has changed (Marginson & Considine 2000, Slaughter & Rhoades 2004).

As more benefits are expected, many governments have turned their gaze towards commercial or economic outputs received from publicly funded research. One outcome of this has been the establishment of technology transfer offices and support for commercializing research results and spin-off companies in universities (Owen-Smith & Powell 2001).

Especially in the US, changes in patent legislation, known as the Bayh-Dole Act (1980), encouraged university-industry collaboration and the commercial use of research results (Mowery et al. 2001: 2004). The implementation of the act has raised debate on whether it provided a boost to patenting since there had already been an increase in US patenting before the Bayh-Dole Act. Scholars argue that the Act has served more as an institutionalizing of university patenting (Kenney

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& Patton 2009, Popp Berman 2008). In any case, many countries in the OECD adopted this model as a basis for patenting and technology transfer (Mowery &

Sampat 2004, OECD 2003, 35). Finland followed suit in 2007 (Ejermo &

Toivanen 2018).

The OECD explained the efforts to commercialize research results to be the result of transformations taking place in “the knowledge-based economy” (OECD 1996). In the 1990s, innovation policy partly replaced science and technology policies and many countries adopted a new conceptual framework called the National Innovation System (Miettinen 2002: 2013, Godin 2006). In the knowledge-based economy, universities play an integral part as the diffusion of knowledge and technology takes place through knowledge networks and national innovation systems.

In the 2000s, the EU made an effort to create a tool for identification, measurement and comparison of third mission activities (Final Report of Delphi Study 2011, Marhl & Pausits 2011). However, efforts to categorize and define the third mission or social impact of research have been difficult. In addition, large frameworks for research assessment may produce a heavy and expensive system with limited use in practice (Martin 2011). Lately, research impact assessment has moved towards interactional approaches which emphasize the use of research results by outside stakeholders such as policy makers and business partners (Spaapen & van Drooge 2011). Against this backdrop, there has been a new understanding of the differences between disciplines and their role in solving various kinds of social problems. This will help policy makers and evaluators to understand what social impact means in different areas of life.

In this dissertation, I address the social impact of academic research from the viewpoint of societal problem solving, scholarly knowledge and research practices. My focus is at the level of research groups instead of focusing on universities as organizations. This approach means that I emphasize the specificity of problem solving and social impact in different disciplines. I focus on the science-society interaction in the educational and social sciences, because scholars have noted that the ‘impact agenda’, auditing, performance measurement, evaluation, and the very concept of impact itself are problematic when it comes to the value generated by the humanities, arts and social sciences (Belfiore 2015, 98–

100). Surprisingly, the educational sciences are not mentioned on their own in the literature. The models for impact evaluation have favored the hard sciences and fields that are more entrepreneurial (Benneworth 2015, Benneworth & Jongbloed 2010). As there are great disciplinary differences in theoretical approaches, methodological choices, and motives of research (Albert 2003), an aim of this dissertation is to show that societal motives are an incremental part of research work but manifest in diverse ways in different phases of the research process.

This dissertation therefore contributes to debates on the role of scholarly knowledge and research in society. I will discuss the social impact of research

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through case studies from two fields of research: research on learning difficulties, and research in the field of multidisciplinary urban studies. I followed two research groups and their research results and demonstrate how the forms and mechanisms of social impact have developed in these fields. At the same time, I suggest placing more emphasis on the different dimensions of impact, the contexts of social impact and the possible expansion of impact as well as institutional stakeholder connections specific to the cases in question.

In the second chapter, I address the changes that science systems have encountered since the 1980s as I discuss how the development of innovation policy has affected both science and universities by producing expectations of social accountability. This chapter also looks at the social impact from the perspective of research evaluation and presents an overview of the changes in Finnish science, technology and innovation policy. In Chapter 3, I explain the attempts to conceptualize the changing science-society relationship by presenting two models which describe the changes in knowledge production, and by providing a short review on the issues of the public understanding of science.

Following this, in Chapter 4, I present the theoretical underpinnings of my study, and in Chapter 5, identify my research questions. In Chapter 6, I describe the empirical cases and my methodological and analytical approaches. Chapter 7 gives an overview of the four articles used in this dissertation. Chapter 8 draws together the findings of my study and Chapter 9 presents a reflection on the research process, transferability of the results and ethical issues. Chapter 10 offers conclusions based on this study.

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2 Changes in the research, development and innovation policies

In this chapter, I will provide an overview of the developments in global research, development and innovation policies. I start with discussions about the so-called linear model of innovation and describe the move towards the interactional models of innovation. Second, I take up the universities’ role as organizations and pay attention to collaboration and interactions between universities and their outside stakeholders, pointing out that there has been more emphasis on the commercial aspects of producing science-based innovations as a source for the economy.

Third, I discuss both policy making and approaches developed in research evaluation and impact assessment research. These debates are close to the political debates which have contributed to the definitions and measurement of social impact. Fourth, I take up the developments in Finnish science, technology and innovation policy.

Since there is no common definition of social impact, this chapter provides the background for broader developments in innovation policy goals, which in part explains the demand for the social impact of research as part of university missions. The chapter ends with some remarks on the intermingling of research, development and innovation policy goals.

2.1 From the linear to interactional model of innovation In 1945, after the Second World War Vannevar Bush’s report Science: The Endless Frontier introduced the idea of “science-push”. In this model the government funds basic research, which then produces benefits in wealth, health and national security. The ideas of Bush have retrospectively been seen to mark the beginnings of modern science policy and his report is said to include the rudimentary ideas of the linear model of innovation (Godin 2006). The linear model means that basic research leads to applied research, and after that to technological development and innovation (Martin 2003, 2–3). Therefore, it is in the state and the government’s interest to fund research to receive benefits from it. However, as pointed out by Bush (1945, 7), the academic community should oversee selecting issues of scientific interest. This type of “social contract” meant high autonomy for universities with their own peer-review system directing the allocation of resources.

In short, the linear model of innovation explained the diffusion process from basic research to applied research and development, and finally to production and diffusion. Scholars studying innovation have pointed out that as such, the linear model is an ideal model, which does not depict reality (Kline & Rosenberg 1986,

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Fagerberg 2003). Among others, Godin (2006, 659–660) argues that the model is not an invention of Vannevar Bush, who merely distinguished between basic research and applied research. Godin argues that instead, the linear model is based on the ideal of pure science and political rhetoric, which made applied science dependent on pure science. In addition, Godin explains the linear model’s success by statistics, which crystallized the model and gave it strength to survive even though this model, is hard to find in real life (Godin 2006, 641).

The linear model of innovation was not the only model for explaining innovation although it remained dominant for quite some time. In the 1960s and 1970s, innovation studies were preoccupied with whether it was the opportunities created by science (science-push) or the demand from the market (market or demand-pull) that described the development of innovations (Miettinen et al.

2006, 40). Godin & Lane (2013) have shown how in the 1980s, the above- mentioned models became identified as alternatives: Schumpeter’s technology- push and Schmookler’s demand-pull, even though the demand-pull model assimilated to multidimensional models (Godin & Lane 2013, 633). The demand- pull model was described as need-pull model by Langrish et al. (1972, 72–74) and presented by Rothwell (Rothwell & Zegweld 1985, 49) as an opposite of technological-push model.

Especially in the US the question of the role of basic research in technical development was debated when the Department of Defense published the results of Project Hindsight stating that basic research did not play a significant part in the development of weapons systems, which were largely based on applied research (Godin & Lane 2013, 624, Miettinen et al. 2006, 40). Another two studies, TRACES and Battelle (following TRACES) by the National Science Foundation, supported the traditional model of basic research leading to innovation. These studies reflect in part the debates of that time revolving around the public funding of science. As Mowery & Rosenberg (1979, 140) have stated, it is important to separate market demand from almost unlimited human needs.

However, in their discussion on the demand-pull model Godin & Lane (2013, 642) distinguish between three meanings of demand² and argue that the models of innovation do not consider human and social needs but exclude them. In this dissertation, I have taken up these distinctions as I find them to be important antecedents of the more recent debates on social impact. Godin and Lane (2013) suggest that (market)demand fits into economic theory and models much better than the idea of (human) need. Therefore, need is not of much interest to

2 The first meaning refers to economic or market demand (demand for a product as a function of price or demand as economic conditions and factors). Second, the social meaning of demand refers to human and societal needs articulated by government organizations. Third, there is a loose meaning of demand as part of a semantic or emerging discourse placing contribution to innovation of factors external to scientists’

pure motivation. (Godin & Lane 2013, 642.)

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researchers and it was substituted with demand, which was understood in a narrow way instead of defining it more broadly. In many ways, human needs are at the heart of discussion on social impact of research.

As the interactive model of innovation gained a foothold, scholars started to discuss “couplings”: how to couple scientific discoveries with (market) needs (Godin & Lane 2013, 626). Freeman spoke of innovation as a “process of coupling”, which takes place in interactions between science, technology and market (Freeman 1979, 211). Therefore, interactions between institutions and other actors became important in creating innovations. Rothwell also described an interactive model of industrial innovation, which regarded innovation as “a sequential process subdivided into a series of functionally separate but interacting stages” (Rothwell & Zegweld 1985, 50).

In the policy field, Donald Stokes (1997) introduced the notion of use-inspired basic research to account for the changing context of science and technology policy. Stokes (1997, 5) stated that“Bush’s ideas on the essential goal of basic research gives too narrow an account of the motives inspiring such work” and

“too narrow an account of the actual sources of technological innovation.” The study of social impact of research is interested in the expected goals of research as well as the processes that lead to impact. Raising the question of whose goals count, Stokes argues thatuse-inspired basic research depicts a situation in which both understanding and use are of interest (ibid. 78–79). This concept also challenges the perspective of how long it takes basic research to reach application.

In some cases, basic research has a near term application as demonstrated by Pasteur’s work: his work in microbiology was quickly applied to public health and industrial problems (Stokes 1997, 83). In other cases, the time required for application is much longer and depends on several social conventions. This is the case in medical science in which the development of products and drugs requires multiple testing and safety protocols and regulations dictate the use of application (Woodcock 2007).

Innovation policy was based on evolutionary economics of innovation in order to provide an alternative to neoclassical economics, which did not consider technological change in its explanations of economic growth. It also formulated the so-called systems view of innovation. The systems view and the concept of the National Innovation System (NIS) were developed by Bengt-Åke Lundvall and Christopher Freeman in connection with OECD policies (Fagerberg &

Verspagen 2009). A neo-Schumpeterian idea of economic cycles caused by technological revolutions replaced neoclassical growth models, and the idea of interactive learning replaced the idea of rational choice. The idea that the sources of innovation derived from basic research, which provide the basis for applied research and technological development, was gradually replaced by a National Innovation System which emphasized an interactionist approach. The OECD published reports in which the formation of innovation policy was developed

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(OECD 1980, 1992). Through the OECD reports, the NIS framework was distributed broadly and affected innovation policy in the European Union (Mytelka & Smith 2002).

In the 1980s, globalization of national economies and changes in industrial policies lead to what some policy researchers have understood as stronger intermingling of science and industry (Etzkowitz & Leydesdorff 2000). This affected science and technology policy guidelines around the world and universities became central actors in promoting societal development and national economy. Instead of the ideals of the 1970s, when universities were regarded as institutions that should aim to provide an intellectual space for researchers, they now had to innovate and orient themselves towards regional development activities (Zomer & Benneworth, 2011, 81–82).

In the wake of economic pressures and commercialization that took place first in the US, universities in Europe followed suit (e.g. Netherlands, Sweden) and started to generate revenue from their educational, research and service functions, which raised concerns of commodification of science among scholars (Jacob 2009, Jacob et al. 2003). As economic constraints on public funding of universities increased, many countries applied a variety of similar policy measures to verify the benefits of research to society (Nedeva & Boden 2006, Jongbloed et al. 2008).

This affected both the organization of science and the role universities are supposed to play in society.

In the US, the Bayh-Dole Act of 1980 aimed to boost university-industry cooperation, technology transfer and patenting. Following the Act, technology transfer offices and industry sponsored research activities were established. The Act was one factor in enhancing university-industry linkages, and as such, both an outcome of and a response to the changing policy climate (Grimaldi et al. 2011, 1046). However, there is variety between the academic disciplines when it comes to patenting and most US patenting and licensing took place in a narrow set of research fields, such as the biomedical sciences or engineering (Cohen & al. 2003, 133; Mowery et al. 2004). Nevertheless, the Bayh-Dole Act served as a model for many other countries and the OECD (2000, 2003) promoted it as an example for other countries, which in turn adopted similar frameworks in the hope of increasing productivity and innovation. For example, Denmark, Germany, France and Finland followed the model to some extent (Mowery & Sampat 2004, 123, Geuna & Rossi 2011; Lissoni et al. 2009; Iversen et al. 2007).

In the tradition of technology assessment that started in the 1960s and 1970s, the social aspects of technology were discussed. However, the assessment methods were technology-driven and not capable of handling conflicting interests and values (Rask et al. 1999, Brooks & Bowers 1970). The aim of technology assessment was to predict and control the social impact of technology but was criticized for not involving deeper scientific understanding of the basis of technology (Brooks 1994). Recently, at the EU level, the social consequences of

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technology have been discussed in the fields of genetics and nanotechnology (Ethical, Legal and Social Aspects/implications), and the EU has launched Responsible Research & Innovation (RRI) action as part of its Horizon 2020 program to promote science-society interaction in areas of public engagement, open access, gender, science education and ethics. Because the traditional evaluation culture has been driven by decision-making in policy and methods, which assume a linear relationship between program resources and outcomes, it has tended to produce backward looking and atomistic indicators (Nieminen &

Hyytinen 2015). To consider the complexity and interconnectedness of socio- technical change, new approaches stress the systemic perspective and integrative evaluation approach (Hyytinen 2017). These approaches reflect the attempt to count the broader social consequences of technology and as such are related to the science-society interaction that will be addressed next.

2.2 From the Third Mission to social impact evaluation

Policy discussions at the OECD and EU level have created a supranational policy framework focused on innovation, knowledge transfer and the expected roles of universities and the organization of research activities (van Vught 2009, European Commission 2007). Regarding the missions of universities, Zomer & Benneworth (2011) argue that against the backdrop of innovation policy and the development of national innovation system viewpoint, universities have responded to external pressures by becoming more engaged with external agencies and actors in society.

Alongside the efforts to profit from research results in the form of patents and commercial products, there has been a shift to measure not only various outcomes but also make universities more ‘accountable’ through different kinds of performance indicators (Shore 2008). This has had an effect on the evaluation of research and universities as organizations.

The terms ‘third mission’ or ‘third mission activities’ have come to mean universities’ engagement with outside stakeholders, usually business partners or industry. Zomer and Benneworth (2011) have explained the rise of the third mission in connection to higher education reform. They see it as a semi- autonomous process and name four general drivers or societal shifts behind the third mission (ibid. 82–86). These include the funding crisis after the Second World War, the liberalization and the commodification of scientific knowledge and the rise of neo-liberalism, the changing nature of knowledge production as scientific problems become more complex, and competitiveness and the urgent imperative of usefulness.

On the third mission terminology, Zomer and Benneworth (2011, 83) state that the OECD’s Centre for Educational Research and Innovation (CERI) introduced the notion of third mission activities in 1982 to account for innovative practices

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in entrepreneurial universities. More recently, the OECD has aimed at benchmarking indicators and developing frameworks to account for industry- science interactions and developed further concepts such as the Knowledge Triangle to describe the connections between research and technology, education, and innovation (OECD 2016, see also European Commission and OECD 2012).

The European Union, in turn, has used the term third mission to describe activities that emphasize the dissemination of research results, cooperation between universities and enterprises, and a new type of accountability (European Commission 2003, EUA 2019). It has also called for a stronger intermingling of research and innovation to set EU-wide research and innovation missions and to form an ‘ecosystem’ which ensures added value and impact (European Commission 2018). In addition, the European Union tried to create standard indicators and a tool for identification, measurement and comparison of third mission activities in a project called European Indicators and Ranking Methodology for University Third Mission (Carrión García et al. 2012, Marhl &

Pausits 2011). In this project, three dimensions were suggested for the third mission: continuing education, technology transfer and innovation, and social engagement (Final Report of Delphi Study 2011). The last of these dimensions highlighted the societal nature of the third mission.

In the policy realm, the third mission is a concept which emphasizes the benefits and returns of public expenditure on science through innovation, technology transfer and entrepreneurial activities. However, the critics of this policy have argued that the third mission is part of the neo-liberal rhetoric of the state for usefulness of science, and as such, it expresses the drive for control and immediate application (Nedeva & Boden 2006, 275; also, Roper & Hirth 2005).

Thus, in the policy discussions, efforts to categorize and define the third mission show how elusive the concept is, and how much it is connected to the economic aspects of understanding impact. From the perspective of university policy, the evaluation frameworks developed at the national level focus on distributing funding to universities, but they do not address research practices or actual research work done by research groups.

The related research literature, which connects to policy making and its analysis, includes both criticism against policy choices but also research that has assumed concepts from the policy debates and developed those concepts further thus increasing the interaction between research and policy making. However, Donovan (2011) has noted that there has been limited consultation between policy makers and research evaluation community but also that there is a lack of engagement between research evaluation specialists and the academic community. According to Donovan, this has led to limited learning at the policy level. In some countries, research evaluation has led to massive evaluation frameworks that risk becoming expensive and too complex for their intended purpose (Martin 2011).

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In the Netherlands and in New Zealand, impact evaluation has been developed extensively (Donovan 2008, Mostert et al. 2010). In addition, in national research assessment exercises such as the UK Research Excellence Framework (REF) and Excellence in Research for Australia (ERA), social impact is incorporated in the evaluation systems (Bornmann et al. 2016). At the same time, the newness of impact as a criterion has meant that not much is known in terms of how peer review processes work in relation to such a criterion even though there are ongoing attempts (Samuel & Derrick 2015).

Scholars who analyze social impact and developments in policy making, have long been aware of the conceptual and methodological challenges of measuring impact (Godin & Dore 2004, Martin & Tang 2006). Some of the most persistant difficulties are time lag and attribution problems when it comes to research and its impact (Spaapen & Drooge 2011). While the OECD countries have a standard classification to measure public R&D expenditure, and while economists have focused on the impact of research and development on economic growth, the non- economic impact of science is limited in the literature (Godin & Doré 2004). In the evaluation practice and indicator development, three phases of impact evaluation have been distinguished (Donovan 2007, Bornmann 2012):

technometrics, sociometrics and case studies. As for the developments in research evaluation regarding social impact, Bornmann (2013) has identified three ways of defining it in research evaluations since the 1990s. These also describe the methods used in evaluation. Following Bornmann’s distinction, De Jong et al.

(2014) have suggested a more detailed characterization. Table 1 presents the above-mentioned phases of impact evaluation and the definitions of social impact in research evaluation. The table is to give a rudimentary description of the development of research evaluation. It is not by any means an exhaustive description of evaluation as a field or evaluation practices.

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Phases of impact evaluation and methodology

Features of social impact evaluation

Definitions of Social impact

Technometrics Data from

commercialization, industry and technology transfer

Product (output)

distinguished as information, tools, instruments, methods and models

Sociometrics Attempts for societally relevant measurements but failure to incorporate cultural import of research in assessments

Knowledge use (societal references): Interaction processes that may result in the adoption of knowledge by a range of social stakeholders

Case studies Combination of qualitative and quantitative

approaches. Used to evaluate specific funding initiatives and not adapted to nationwide assessments but used as part of national evaluation frameworks

Societal benefits or social change such as impact on professional practice, impact on policy, economy, impact on culture and environment.

Sensitivity to the definitions depending on the end user

Table 1. Phases of impact evaluation and definitions of social impact in research evaluations since the 1990s, based on Donovan (2007), Bornmann (2013) and DeJong (2014)

Godin and Dore (2004) have argued that there is a misunderstanding between output and impact. They point out that output is a direct result of research activity, whereas impact is the indirect effect of science on society. They continue (ibid. 9) by clarifying that output is for example knowledge measured by papers, innovation, and trained people whereas impact is the effects of their use in reality.

Another point that Godin and Dore make is that of knowledge transfer mechanisms, more specifically the diffusion and appropriation of knowledge. It is one thing to spread knowledge or hear about it; it is another thing to use it.

Compared to scientific impact measurement, the research on social impact measurement is recent and not as well structured (Bornmann & Marx 2014). The frameworks developed to measure the social impact of research have emphasized the actual use of research results by other stakeholders. In the attempts to measure

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social impact, research evaluation has moved towards interactional frameworks and a broader definition of social impact. The recent interactive approaches in research evaluation have attempted to answer is the question about the contribution of research in terms of social change and interaction (van Spaapen &

Drooge 2011, van Drooge & Spaapen 2017). Such attempts, e.g. SIAMPI³ (Social Impact Assessment Methods through the study of Productive Interactions) have developed methods to assess social impact of research projects, research programs and research funding instruments.

Some approaches, such as PIPA⁴ (Participatory Impact Pathways Analysis), are project management approaches for development research based on the logic models that involve a range of stakeholders. In general, logic models of evaluation work as planning and evaluation of diverse projects and they are often related to program theory and complexity theory (Rogers 2008). For example, Payback Framework (Donovan & Hanney 2011) was developed as a tool for data collection in order to account for the impact or payback of health service research. As such, the logic-models in evaluation still rely on a linear and causal relationship between intervention and impacts making evaluation normative and value-laden (Patton 2011). There have been recent suggestions to account for a multi-method approach which would combine evaluation, foresight, system dynamic modelling and simulation, and societal embedding that would support decision-making in various policy and decision-making situations (Nieminen & Hyytinen 2015). In addition, public value mapping (Bozeman 2003) and assessment of public value (Molas-Gallart 2015) have focused on interactions between researchers and stakeholders from the viewpoint of public value creation and formative evaluation. Recently research evaluation has placed more emphasis on the humanities and social sciences by providing typologies of impact pathways in these fields (De Jong & Muhonen 2018, Benneworth 2015, Muhonen et al. 2019).

However, the educational sciences have not been extensively discussed in the research literature, and as mentioned earlier, most of the literature does not address research practices and research groups’ work leaving out important aspects of social impact. My attempt is to provide a more analytical orientation into the discussions of social impact.

The above-mentioned evaluation approaches attempt to draw together various methods and analysis techniques in order to provide a more contextual view on social impact of research and research organization (Joly et al. 2015, see also Bornmann 2013). In these contexts, stakeholders are an important part of research utilization which is why these approaches reflect an attempt to provide holistic frameworks for impact evaluation and the inclusion of stakeholders. However,

3 http://www.siampi.eu/

4 PIPA was developed from earlier ideas in program theory and pioneered within the CGIAR Challenge Program on Water and Food with support from the CGIAR Institutional Learning and Change (ILAC) program. See Douthwaite et al (2007)

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these frameworks have still focused on research projects or programs as the unit of analysis.

2.3 Developments in the Finnish science, technology and innovation policy

Finland has been described as a latecomer regarding industrialization; the main structures for supporting scientific and technology research were established in the 1960s and 1970s (Miettinen, 2013). Lemola (2003) has separated three phases in Finnish science, technology and innovation policy: the formation of basic structures in the 1960s and 1970s, the era of technology policy in the 1980s, and the era of innovation policy in the beginning of the 1990s. In addition, the period from 1980s to 1990s has been described as a shift from the welfare state towards the competition state in which market-orientation and competitiveness were postulated as government targets (Pelkonen, 2006, Yliaska, 2014). In the 2000s, the competition state was transformed into an idea of accumulating innovation benefits especially in terms of innovative regions, which eventually formed a metropolis state (Moisio 2012, 225–245).

In the 1950s and 1960s, a traditional academism prevailed in Finnish scientific life, but the 1970s saw the advancement of welfare state objectives and ‘social relevance’ of research as well as system expansion (Nieminen 2005, 44, Kaukonen & Nieminen, 1999, 174). Technology policy developed separately from science policy but gradually technologization of university and science policies, advancement of innovation policy, and neoliberal emphasis of governments⁵ affected universities and the political organization of science and technology (Pelkonen, 2001).

In the 1980s, technology policy received more resources and the establishment of the Technology Development Center (Tekes) in 1983 marked the birth of an important governmental funding organization. Through its technology programs, Tekes, later named the National Technology Agency, achieved a central position as an instrument of technology policy (Kaukonen & Nieminen 1999, 174). In addition, the Science Policy Council, which had been established in 1963 to strengthen the then developing policy, was renamed the Science and Technology Policy Council in 1987. It established a high-level body for combining science policy and technology policy (Pelkonen, 2008, 60, Pelkonen, et al. 2014).

Throughout the years, both organizations have undergone changes in their functions, accompanied by a reorganization of their activities. In 2008, the Science

5 Especially Prime Minister Holkeri’s government 1987–1991 and Prime Minister Aho’s government 1991–1995

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and Technology Policy Council was renamed Research and Innovation Council, and in 2018, Tekes became Business Finland.

Finland was among the first adopters of the concept of the National Innovation System (NIS) and it imported other concepts such as knowledge-based society from the OECD, especially between the 1960s and 1980s (Lemola, 2003, 62, Kaukonen & Nieminen, 1999, 175). The concept of the National Innovation System stresses the systemic, interactive nature of innovations and integrates a range of factors, including organizations, enterprises and institutions in the process of creating them (Schienstock & Hämäläinen 2001). The Science and Technology Policy Council adopted the National Innovation System concept in 1990 and elaborated on the concept in its subsequent reviews⁶ (Miettinen, 2013, 54). Previously, in the 1987 review of science and technology policy, Erik Allardt, the Chair of the Academy of Finland’s Central Committee stated that even though resources were rightly directed at the technical sciences, at the same time there was an increased need for the humanities and social sciences to understand the changes that these technical developments brought with them. He pointed out that it was important to separate the speed of technical development and its long-term impacts. Allardt was especially concerned about the role of democratic procedures, changes in the work life and public services. However, since the 1987 review, the emphasis has been on innovation.

Adopting the concept of the National Innovation System meant that the economic and commercial aspects gained a foothold in science and technology policies dragging a market-oriented approach into the academic realm. This viewpoint emphasized commercialization, collaboration between universities and companies, and productization of research and educational activities (Pelkonen 2008, 64.) Finally, membership in the European Union in 1995 connected Finland to the research and technological development (RTD) programs.

Universities’ social impact in Finland

From the viewpoint of higher education policy, universities have a service function and as such, the building of a broad regional university system in Finland has played an important part in increasing human capital and educating people for the labor market (Nieminen 2005). Therefore, the impact of universities has been debated from the perspective of the welfare state and the regional role of universities (Virtanen 2002). Even though the terms “third mission” and “social impact” have not been used until recently, they have been part of the Finnish higher education policy debates since the university network was established.

Kivinen et al. (1993) have noted that from the 1980s onwards, higher education policy was directed towards responding to economic changes instead of

6 Towards an Innovative Society 1993, Finland: A Knowledge-Based Society 1996, and The Challenge of Knowledge and Know-How 2000

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connecting it to the broader social policy goals, which had characterized the 1960s and 1970s. This also meant that in universities a performance-based evaluation reporting system was established to monitor their functions and accomplishments (Kivinen et al. 1993, 150–151).

As the university-industry-government link was strengthened both at the level of policy concepts, policy implementation and institutional change, the universities became embedded in the national innovation system (Kaukonen &

Nieminen 1999, 174–175). At the same time, there was pressure to increase universities’ performance because they were now partly responsible for national competitiveness. The Finnish National Fund for Research and Development (Sitra) was involved in setting up five technology transfer offices in the 1990s (Tupasela 2000). In 2007, Sitra changed its name to the Finnish Innovation Fund and is now promoting Social Impact Bonds (SIB) to reform public services and provide impact, especially at the municipal level.

From the 2000s onwards, universities have faced increasing pressures of competitiveness and performance (Kivinen et al. 1993, 219). The so-called third mission was included in the Finnish University Act (714/2004) in 2005. The suggestion for the amendment came from the memorandum of the Working Group of Regional Development of Higher Education Institutions (4.12.2001). The Working Group’s suggestion and the government proposal (HE10/2004) stressed the role of universities in regional development, including the dissemination of research results through new types of business activities. Therefore, the third mission has emphasized the commercialization of knowledge, though individual universities have approached the societal expectations in diverse ways (Nieminen 2005, 118). The Act itself states that the universities are obliged “to advance lifelong learning,to interact with the rest of the society, and to advance the social impact of research results and artistic activities”. As such, the act does not consider social impact or social interaction as a separate mission, but it is fulfilled through the missions of teaching and research.

Another legislative change, the University Act reform in 2010, changed the legal status of universities and gave them more financial autonomy by separating them from the state (HE7/2009, University Act 558/2009). In addition, the reform placed more administrative power in the hands of individual directors (rectors, deans, department managers) instead of the election-based, collective decision- making administrative organs. The evaluation of the new University Act of 2010 concluded that the new act had started a significant structural and cultural change in management practices of the universities (OKM 2016, 76–77). These new practices evoked tensions and escalated differences of opinion between the management and employees. Around the same time, the government introduced cuts in university funding and some universities performed broad organizational changes, employee co-operation negotiations and layoffs. In 2016, 4000

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university employees signed a petition to invoke a counter reaction to the changes (Heinonen et al. 2016).

As for the social impact of universities, the evaluation of the University Act stated that social interaction became topical, mainly because there were now more

‘outside’ representatives on the boards of directors of universities (OKM 2016, 65–71). Especially at the level of management, social interaction became important. Another development was that universities now raised outside funding and had more connections with businesses.

Ståhle (2013) suggests that the recent changes have led to a situation in which the ‘logic of market’ and the ‘logic of science’ create contradictory incentives for the universities. This means that on the one hand, research resources are directed by the market logic and the allocation of money is based on innovation policy goals to collaboration between businesses and universities. On the other hand, researchers follow the logic of science as the universities recognize the incentives to publish in internationally established journals. In practice, as Hautamäki &

Ståhle (2012) note, the current university funding model of the Ministry of Education and Culture does not include indicators to guide the universities’

innovative activities. The same goes for social impact; the government has not adopted indicators of social impact as part of its university funding structures.

Even though a researcher could be an active influencer outside academia, this impact goes unnoticed (Hautamäki & Ståhle 2012, 74).

Against the fact that there is an existing system of indicators for performance assessment, the indicators of social impact have been scarce. There have been projects initiated by the state during the social impact of universities has been discussed (Lemola et al. 2008, Ritsilä et al. 2007). Recently, the Academy of Finland’s Strategic Research Council was established to fund high-quality research that has great societal impact. The Council functions to support interaction between researchers and society, to advance the diversity and interdisciplinarity of research, and to promote research themes that address the major challenges facing society (Mickwitz & Maijala 2015). As such, the establishment of the Strategic Research Council reflects the need for a broad and diverse research base to support public policy and decision making and it can be seen as an attempt to strengthen evidence-based policy (SRC 2018). Additionally, the Academy of Finland has increased the role of impact in its funding decisions and reporting.

In addition, the latest guidelines of the Finnish Research and Innovation Council (2015–2020) call for strengthening the social impact of research. Impact is mostly understood as application and a new feature is encouraging entrepreneurship in higher education institutions (ibid. 17). The guidelines mention measuring impact as a crucial aspect in order to distribute funding, as well as developing evaluations to achieve these measurements. In addition, the guidelines promote more effective commercialization of research results and

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facilitating the process of translating R&D results into start-ups. Research organizations are also expected to improve their business skills and the public sector to take a more active role by “reinventing itself” (ibid. 18–24).

2.4 Summary: Remarks on the policy changes

Starting from the Second World War, science, technology and innovation policies have become more pronounced as governments seek to make explicit the contributions of publicly funded research to the society. In many countries, technology and innovation policies now provide a general framework for economic growth and innovation has become a core aspect in other policy fields too. The so-called linear model of innovation has been replaced by a more interactive model, which emphasizes engagement between science and society, mostly in the form of interaction between universities and industry.

Especially in the US, the Bayh-Dole Act emphasized technology transfer and the economists turned their interest to the innovation process. In Europe, many countries followed suit and assumed the Bayh-Dole model in some form in their national policy framework. Another concept that was developed from the policy discussions was the National Innovation System, which replaced the linear model of innovation by suggesting a more interactive view.

Following the globalization of national economies, universities have faced new expectations regarding funding, organizational structures and engagement with regional actors. They are now scrutinized in terms of their performance and impact. Accountability and auditing have become global practices after the rise of new public management (Shore 2008) but also different types of social, cultural, environmental and economic returns of research are assessed in national research evaluation frameworks (Bornmann & Marx 2014).

In the research and policy literature, there is a diverse vocabulary on the issue of social impact: third mission, engagement, outreach, valorization, to name a few (Roper & Hirth 2005, Jongbloed et al. 2008). Regardless of the complex terminology and the difficulties in defining social impact, it has become more pronounced in policy discussions (Pinheiro et al. 2015). However, the research evaluation practices tend to focus on project evaluation or evaluation of organizations rather than on longer time perspectives. Until recently, they have focused on research fields representing natural and engineering sciences, which leaves out the humanities, educational sciences and social sciences. Many evaluations have focused on evaluating institutions, research programs or they are based on peer evaluation of research quality. Since the 1990s, values and broader social benefits have received more attention both in policy debates and in evaluation practices. This has resulted in more interactional approaches in

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evaluation emphasizing the social, cultural, environmental, and economic benefits of research to society.

In Finland, the changes have taken place later than in other countries but the demands for economic and social impact had already been discussed when the education system was established. Therefore, the social impact has been mostly an issue of regional balance. However, Finland adopted the idea of a national innovation system among the first countries in the world, and the current policy changes have stressed the role of innovation in many social spheres, such as public services. While any policy led by government should be stating its values and starting points, questions arise concerning the relationship between different policies. The problem is that by assuming that innovation policy is somehow more significant than other policy fields, other policy objectives might be neglected.

For that matter, Finland provides an interesting opportunity to study the social impact of research as it has both adopted the innovation policy framework quickly and has a long tradition of welfare state policies that guide the development of the educational system and health care system. These policies have stated values of universalism and equity.

In the next chapter, I will present attempts to conceptualize the changes described in Chapter 2, after which I outline the theoretical premises that have guided my work.

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3 Attempts to conceptualize the interaction between science and society

In the field of science, technology and innovation studies, scholars have suggested that there are changes taking place in science systems and their social contexts.

These interpretations and analyses have typically been interested in the changes of the relationship between science and society and the increasing connections between universities and other stakeholders, not forgetting the public role of science in legitimizing its very existence. Since the social impact of research falls into the realm of university organization, and as such is an incremental part of these debates, I find it important to provide background on the concepts used in policy and research discussions. The aim in this chapter is to provide a link between the broader policy developments described in previous chapters, and attempts to conceptualize them, both academically and politically.

In this chapter, I will present the conceptualizations that have approached the questions of interaction between science and society proposing a rupture in the way scientific and scholarly knowledge is produced and used. However, I understand these models to represent types of ‘diagnosis of the era’ theorizing. In sociology, these explanations are considered to be a specific genre of theorizing (Noro 2000, Tuunainen 2013) as they help us to make sense of what is going on in the society. As such, the diagnoses of the era do not represent research theory nor general theory, but they provide insights and orientations into our present lives.

I start with a general overview on approaches discussing changes in science- society interaction and knowledge production, after which I will focus on two models in detail: Mode 2 knowledge production, and the Triple Helix model.

These models have described university-industry-government relationship, the birth of entrepreneurial universities, and contextual, robust science. I have chosen these two models because they have been discussed in relation to research evaluation and the interactional models stressing “contextual and robust data”

which would support social impact evaluation (Bornmann 2013). The models suggest significant changes in the ways science is done. Lately, there have been attempts to combine these two models, which have evolved towards explaining the formation of networks, clusters and even ecosystems based on interaction, connectivity and complementarity (Carayannis & Campbell 2009). After discussing the models, I will look at the literature that has focused on scrutinizing the public understanding of science from the perspectives of the audiences, citizens and the broader public.

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3.1 Explaining the change in knowledge production

The Mode 2 and Triple Helix models are part of a broader discussion that attempt to account for the changes related to science systems and knowledge production.

Several approaches reflect and explain the changes of science-society interaction (Hessels & van Lente 2008). Among these, some (such as post-normal science), have presented discussion about risk and uncertainty in environmental policy questions and suggested increasing public participation and public engagement to ensure scientific quality in decision making (Funtowitcz & Ravetz 1993). Others, such as post-academic science (Ziman 2003) and academic capitalism (Slaughter

& Leslie 1997), addressed the changing norms of science and the market-driven attempts of academics and universities to commercialize scientific research. In addition, strategic science (Rip 2002) and the finalization thesis (Böhme & van den Daele1976) argued for the need to look at disciplines and their levels of maturity in terms of producing social impacts. Compared to the Mode 2 and the Triple Helix models, these approaches vary when it comes to how they see the changes taking place in science systems and how they suggest steps forward (for a detailed comparison see Hessels & van Lente 2008).

Models that have described the changes affecting universities and research systems, aim to explain these changes in terms of transformation: From a more traditional way of doing science to a new type of distributed or contextualized science. They emphasize that the practices of science have changed and are different from earlier practices. These models also take their point of reference from the overarching concepts of the knowledge economy and knowledge society (Powell & Snellman 2004, Ranga & Etzkowitz 2013).

In their book The New Production of Knowledge, Gibbons et al. (1994) mapped the characteristics of a new type of knowledge production, which they called Mode 2. In the Mode 2 model, knowledge production happens in a new way to meet the changing demands of various stakeholders. Contrary to the traditional model of disciplinary knowledge production (Mode 1), the new production of knowledge is transdisciplinary and created in diverse social and economic contexts. These contexts then differentiate into specialties and become linked with each other through a range of communication networks. Not only do the knowledge production sites become many, but they also include negotiation between different stakeholders, who make specific demands for knowledge to be

“robust” and meet their interests.

Mode 2 stresses the context of application, which suggests that the norms of science have changed to emphasize problem solving and usefulness of research.

This happens to the extent that ‘context-sensitive science’ is now produced in a more open system of knowledge production where each context of application is a ‘transaction space’ at which society speaks back to science (Gibbons 2000, 162).

This idea has been coined in the concept of the agora, describing a domain through

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which people enter the research process and in which research findings are used and disseminated (Nowotny et al. 2003, 192).

Mostly what is described by the Mode 2 model are developments in the fields of science, technology and engineering. The authors take up the question of the humanities and social sciences admitting that many features of the Mode 2 knowledge production, such as contextuality, transdisciplinarity and reflexivity, are characteristic of these fields (Gibbons et al.1994, 99–100). However, the model does not mention the educational sciences and does not elaborate extensively on the case of the humanities, but talks about it from the perspective of popular culture and culture industry, in which commercialization is part of the process of contextualization (ibid. 99). The authors’ examples come within the advertising business and they leave out the institutional aspects of the educational sciences, humanities and social sciences, such as their role in building educational systems and public services even though Gibbons et al. take up higher education as playing part in social stratification.

There have been attempts to elaborate on the Mode 2 knowledge production model in order to discuss the role of knowledge in higher education. For example, Barnett (2004) has taken up the role of education and learning in his suggestion of a Mode 3 type of knowledge, which he calls knowing-in-and-with-uncertainty.

He speaks of pedagogical uncertainties and disturbances when describing what he sees as super complexity within the pedagogical tasks of higher education.

Recently, Barnett & Bengtsen (2017, 9) have suggested that the relationship between knowledge and the university needs reconceptualization. They depict an ecological university, which is sensitive to other ecosystems, such as knowledge, the economy, social institutions, learning, individual persons, culture and the environment.

Some scholars have developed Mode 3 in relation to learning, teaching and research. They explain Mode 3 knowledge to be normative and existential and argue that it answers ‘slow questions’ which are sometimes personal and value- laden (Kunneman 2005 referred to in Isaac 2014, 94). Sandstrom (2014) has distinguished between the use of the Mode 3 concept in the philosophy of knowledge and higher education, and in management, operations and systems thinking. It seems that the recent elaborations within the latter thinking are dominant and they take their point of reference from the concepts of knowledge economy and knowledge society (Carayannis & Campbell 2009). These accounts tend to forget the actual setting where Mode 2 knowledge production was developed. Originally, the Swedish Council of Research and Planning funded the project, and the critics have pointed to its political underpinnings and normativity (Tuunainen 2013, Gulbrandsen & Langfeldt 2004).

In contrast to the Mode 2 model, the Triple Helix overlay suggests providing a model at the level of social structure and to explain Mode 2 as a historically emerging structure for the production of scientific knowledge (Etzkowitz &

Societal Problem Solving and University Research : Science-Society Interaction and Social Impact in the Educational and Social Sciences

Terhi Esko