Another Voice: Feminist Perspectives on Social Life and Social Science.

1977 ◽  
Vol 6 (2) ◽  
pp. 256
Author(s):  
Myra Marx Ferree ◽  
Marcia Millman ◽  
Rosabeth Moss Kanter
Keyword(s):  
Social Science ◽  
Social Life ◽  
Feminist Perspectives

Inventing the Social

2018 ◽  
Author(s):  
Alex Wilkie
Keyword(s):  
Social Science ◽  
Digital Media ◽  
Social Life ◽  
Social Research ◽  
Ways Of Knowing ◽  
Creative Practice ◽  
The Arts ◽  
Starting Point ◽  
The Social

Inventing the Social, edited by Noortje Marres, Michael Guggenheim and Alex Wilkie, showcases recent efforts to develop new ways of knowing society that combine social research with creative practice. With contributions from leading figures in sociology, architecture, geography, design, anthropology, and digital media, the book provides practical and conceptual pointers on how to move beyond the customary distinctions between knowledge and art, and on how to connect the doing, researching and making of social life in potentially new ways. Presenting concrete projects with a creative approach to researching social life as well as reflections on the wider contexts from which these projects emerge, this collection shows how collaboration across social science, digital media and the arts opens up timely alternatives to narrow, instrumentalist proposals that seek to engineer behaviour and to design community from scratch. To invent the social is to recognise that social life is always already creative in itself and to take this as a starting point for developing different ways of combining representation and intervention in social life.

Four Galore? The Overlap between Mary Douglas’s Grid-Group Typology and Other Highly Cited Social Science Classifications

2020 ◽  
Vol 38 (3) ◽  
pp. 263-294
Author(s):  
Marco Verweij ◽  
Petya Alexandrova ◽  
Henrik Jacobsen ◽  
Pauline Béziat ◽  
Diana Branduse ◽  
...  
Keyword(s):  
Social Science ◽  
Research Design ◽  
Social Relations ◽  
Social Life ◽  
Interrater Agreement ◽  
Mary Douglas ◽  
Highly Cited

Recently, neuroscientists have argued that elementary ways of organizing, perceiving, and justifying social relations lurk behind the diversity of social life. In developing grid-group typology, anthropologist Mary Douglas proposed such universal forms. If these are universal, then we could expect other widely cited classifications to overlap with grid-group typology. We tested this expectation by examining to which extent the elements of Douglas’s typology overlap with those of 39 highly influential classifications proposed since 1970. We established overlap by calculating the interrater agreement among 11 coders. Fair to good interrater agreement, despite a complex coding exercise and minimal training, suggests that such overlap exists. Nevertheless, limits to our research design call for further studies. These findings should contribute to a rekindling of the question whether universal forms of organizing and perceiving social relations exist and to a further consideration of whether Douglas has managed to uncover these.

scholarly journals For a heterodox computational social science

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110477
Author(s):  
Petter Törnberg ◽  
Justus Uitermark
Keyword(s):  
Complex System ◽  
Social Science ◽  
Computational Methods ◽  
Social Life ◽  
Critical Realism ◽  
Computational Social Science ◽  
Digital Data ◽  
Neoclassical Economics ◽  
Research Strategies ◽  
Digital Capitalism

The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life.

scholarly journals Contextualizing Social Science in Nepal

1970 ◽  
Vol 2 ◽  
pp. 1-30
Author(s):  
Dev Raj Dahal
Keyword(s):  
Social Science ◽  
Social Life ◽  
Scientific Reasoning ◽  
Social Science Research ◽  
Linear Structure ◽  
Science Research ◽  
Practical Experience ◽  
Contextual Knowledge ◽  
Social Scientists ◽  
The One

Social science informs about the ideals and trains experts to deal with the complex social realities. It has a public purpose rooted in what we call dharma (professional and institutional responsibility) as opposed to the arrogance of reason, self-will and self-rationalization intrinsic to contemporary rational choice and modernity. Learning has a synergy - establishing connection between the world of social science theories and the drama of social life. A lack of mutual learning between Nepal's traditional faith intellectuals and modern reason-based social scientists has created a big hiatus and contradiction. The academic life of social scientists in Nepal is completely outside of spiritual, moral and ethical influence experienced by ordinary public. The spiritual blindness of modern social scientists has thus opened multiple gaps between their worldview and those of the citizens on various frontiers--theoretical knowledge and practical experience, technical understanding and composite knowledge and secularity of social science and the vitality of the Hindu-Buddhist scriptures in the popular mind, culture, behavior and practices. This has reinforced a division between the system of knowledge of social scientists and the life-world of people. The proponents of new social movements in Nepal, such as women, Dalits, Janajatis, Madhesis, youths and marginalized population are seeking a structural shift in reason-based knowledge to both reason and feeling in social science knowledge discovery. This movement can open the "captive mind" to social learning of contextual knowledge, conduct research with the citizens, provide inputs to the policy makers and reverse their linear, structure-bound, rationalist and disciplinary thinking into the one that represents what the Nepal mandala, the Nepali space, is really like and how to improve it for the better. The renewal and indigenization of qualitative social science research is important to overcome the spirited challenges posed by social forces in Nepal and contribute to the application of scientific reasoning in public policy and social change.Key Words: social movement, NepalDOI = 10.3126/dsaj.v2i0.1356Dhaulagiri Journal of Sociology and Anthropology Vol.2 pp.1-30

Computational Social Science, Big Data, and Networks

2020 ◽  
pp. 515-534
Author(s):  
Bruno Abrahao ◽  
Paolo Parigi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Social Science ◽  
Social Life ◽  
Computational Models ◽  
Social Space ◽  
Computational Social Science ◽  
Social Processes ◽  
New Science ◽  
Building Models

The emergence of Big data and a quantified social space has prompted the birth of a new science, computational social science (CSS), whose roots are founded in research aiming to describe social processes using computational models. Big data now fuels rapid advancements in the field, providing the basis for building models and algorithms of human behavior. New sources of massive amounts of data fundamentally reflect interactions, and, in this context, networks are intuitive abstractions to model our social life, especially that mediated by technology. The chapter introduces several examples of empirical and theoretical CSS research employing network analysis, machine learning and online experiments. It concludes with a list of challenges confronting CSS practitioners, in and outside of academia.

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