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Inspired by the South American research tradition known as “social technology,” this article proposes an operational framework to advance the understanding of mechanisms that help to promote social transformation. To illustrate its theorizing potential, we apply the framework to a nonprofit organization–Parole d’excluEs–that was created in Montreal (Canada) in 2006 and that has been promoting citizen mobilization and commitment to social change (parole-dexclues.ca). To that end, we offer a theoretical paper with an empirical illustration as a first step in a reflection on employing a global South theoretical lens–drawing on the concept of social technology–to make sense of a global North social innovation experience and to advance existing knowledge on the mechanisms of social transformation. The results contribute to social innovation research and practice, particularly at the interface between the management and nonprofit literatures.

Inspired by the South American research tradition known as “social technology,” this article proposes an operational framework to advance the understanding of mechanisms that help to promote social transformation. To illustrate its theorizing potential, we apply the framework to a nonprofit organization–Parole d’excluEs–that was created in Montreal (Canada) in 2006 and that has been promoting citizen mobilization and commitment to social change (parole-dexclues.ca). To that end, we offer a theoretical paper with an empirical illustration as a first step in a reflection on employing a global South theoretical lens–drawing on the concept of social technology–to make sense of a global North social innovation experience and to advance existing knowledge on the mechanisms of social transformation. The results contribute to social innovation research and practice, particularly at the interface between the management and nonprofit literatures.

Recent years have seen a surge of interest in prefigurative politics, which refers to the political strategies that model a future society on a micro level and aim to instantiate radical social change in and through practice. While most previous studies have focused on defining the concept and categorizing various types of prefiguration, this paper contributes by investigating under what circumstances prefiguration leads to revolutionary social change. The paper takes an original approach to these issues by turning to transition studies and the socio-technical change literature. This field focuses on the technical equivalence of prefiguration: namely, the relationship between small-scale niche innovations and large-scale technological transitions. Through theoretical discussions and empirical illustrations, this paper presents a typology of five transition pathways through which prefigurative strategies may result in a range of social change outcomes from reformative to revolutionary transformation.

Recent years have seen a surge of interest in prefigurative politics, which refers to the political strategies that model a future society on a micro level and aim to instantiate radical social change in and through practice. While most previous studies have focused on defining the concept and categorizing various types of prefiguration, this paper contributes by investigating under what circumstances prefiguration leads to revolutionary social change. The paper takes an original approach to these issues by turning to transition studies and the socio-technical change literature. This field focuses on the technical equivalence of prefiguration: namely, the relationship between small-scale niche innovations and large-scale technological transitions. Through theoretical discussions and empirical illustrations, this paper presents a typology of five transition pathways through which prefigurative strategies may result in a range of social change outcomes from reformative to revolutionary transformation.

In this chapter I turn to how social science can be adapted to the challenges and tools of the 2020s, becoming more data driven, more experimental and fuelled by more dynamic feedback between theory and practice. Social science at its grandest is the way societies understand themselves: why they cohere or fall apart; why some grow and others shrink; why some care and others hate; how big structural forces explain the apparently special facts of our own biographies. It observes but also shapes action, and then learns from those actions.Starting with the idea of social science as collective selfknowledge, I describe how new approaches to intelligence of all kinds can help to reinvigorate it. I begin with data and computational social science and then move on to cover the idea of social R&D and experimentation, new ways for universities to link into practice, including social science parks, accelerators tied to social goals, challenge-based methods and social labs of all kinds, before concluding with the core argument: an account of how social science can engage with the emerging field of intelligence design. This is, I hope, a plausible and desirable direction of travel.The rise of data-driven and computational social ScienceWe are all familiar with the extraordinary explosion of new ways to observe social phenomena, which are bound to change how we ask social questions and how we answer them. Each of us leaves a data trail of whom we talk to, what we eat and where we go. It's easier than ever to survey people, to spot patterns, to scrape the web, to pick up data from sensors or to interpret moods from facial expressions. It's easier than ever to gather perceptions and emotions as well as material facts – for example, through sentiment analysis of public debates. And it's easier than ever for organisations to practise social science – whether it's investment organisations analysing market patterns, human resources departments using behavioural science or local authorities using ethnography.These tools are not monopolised by professional social scientists. In cities, for example, offices of data analytics link multiple data sets and governments use data to feed tools using AI – like Predpol or HART – to predict who is most likely to go to hospital or end up in prison.

In this chapter I turn to how social science can be adapted to the challenges and tools of the 2020s, becoming more data driven, more experimental and fuelled by more dynamic feedback between theory and practice. Social science at its grandest is the way societies understand themselves: why they cohere or fall apart; why some grow and others shrink; why some care and others hate; how big structural forces explain the apparently special facts of our own biographies. It observes but also shapes action, and then learns from those actions.Starting with the idea of social science as collective selfknowledge, I describe how new approaches to intelligence of all kinds can help to reinvigorate it. I begin with data and computational social science and then move on to cover the idea of social R&D and experimentation, new ways for universities to link into practice, including social science parks, accelerators tied to social goals, challenge-based methods and social labs of all kinds, before concluding with the core argument: an account of how social science can engage with the emerging field of intelligence design. This is, I hope, a plausible and desirable direction of travel.The rise of data-driven and computational social ScienceWe are all familiar with the extraordinary explosion of new ways to observe social phenomena, which are bound to change how we ask social questions and how we answer them. Each of us leaves a data trail of whom we talk to, what we eat and where we go. It's easier than ever to survey people, to spot patterns, to scrape the web, to pick up data from sensors or to interpret moods from facial expressions. It's easier than ever to gather perceptions and emotions as well as material facts – for example, through sentiment analysis of public debates. And it's easier than ever for organisations to practise social science – whether it's investment organisations analysing market patterns, human resources departments using behavioural science or local authorities using ethnography.These tools are not monopolised by professional social scientists. In cities, for example, offices of data analytics link multiple data sets and governments use data to feed tools using AI – like Predpol or HART – to predict who is most likely to go to hospital or end up in prison.

This book efficiently contributes to our understanding of the interplay between data, technology and communicative practice on the one hand, and democratic participation on the other. It addresses the emergence of proactive data activism, a new sociotechnical phenomenon in the field of action that arises as a reaction to massive datafication, and makes affirmative use of data for advocacy and social change. By blending empirical observation and in-depth qualitative interviews, Gutiérrez brings to the fore a debate about the social uses of the data infrastructure and examines precisely how people employ it, in combination with other technologies, to collaborate and act for social change.

This book efficiently contributes to our understanding of the interplay between data, technology and communicative practice on the one hand, and democratic participation on the other. It addresses the emergence of proactive data activism, a new sociotechnical phenomenon in the field of action that arises as a reaction to massive datafication, and makes affirmative use of data for advocacy and social change. By blending empirical observation and in-depth qualitative interviews, Gutiérrez brings to the fore a debate about the social uses of the data infrastructure and examines precisely how people employ it, in combination with other technologies, to collaborate and act for social change.