Rethinking the relations of power in parental sharing on social media
Bibliographic record
Abstract
Brief Abstract As sharing information about ourselves and others online becomes more embedded in social life, surveillance studies has analysed this practice both in relation to structure powers that profit from data, as well as sharing norms. Understandings of power remain largely oriented towards the macro level and do not account for the ways micro-level habits both inform and are formed by power relations. Drawing on findings from in-depth interviews with 17 Danish families in 2017, we argue that habits and desires create conditions of sharing, whilst also being informed by and influencing broader processes such as big data economic logics. Extended Abstract As social life increasingly unfolds in online spaces, the importance of understanding and interrogating the way power relations shape the disclosure of information in these spaces cannot be ignored. Sharing information about ourselves and others online is deeply embedded in social rituals and the surveillant dynamics of this practice have been debated both in relation to structure powers that creates the obligation to share (Birchall, 2017; Fuchs, 2012), as well as micro-level analyses of sharing norms in various contexts such as parenting (Blum-Ross and Livingstone, 2017; Damkjaer, 2018), intimate images (Miguel, 2016), and the construction of online identity (Baym and boyd, 2012; Marwick and boyd, 2014). More recently, surveillance scholars have attempted to theorise the interactions between these processes to understand the operation of power relations beyond the limitations of structure and agency (Christensen and Jansson, 2015; Van Dijck, 2013). However, understandings of power remain largely oriented towards the macro level. This paper seeks to outline a theoretical approach that attends everyday experiences whilst interrogating how sharing practices are constituted through relations of power. Our analysis will draw on findings from in-depth interviews with 17 Danish families during six months in 2017, which focused on how they implement and negotiate the use of digital technologies in their lives. We focus on the data-sharing practices described by parents in our study, which occurred on social media and through the use of smartphone apps. Using a Deleuzian understanding of repetition as generative of creative difference (Deleuze, 1994), we argue that everyday practices of sharing on social media can be understood as habits which, over time, respond to and contribute broader relations of power that constrain and enable capacities. We argue that social media platforms are constituted through these relations of power, which modulate capacities through creating capacities. This analysis is informed by Deleuze’s (1992) theorisation of ‘societies of control’, in which he argues that power increasingly operates dispersed through relations of access and modulation, rather than production and discipline. While Deleuze’s analysis of power has been widely used in surveillance studies to examine changes associated with digitisation (see for example: Ericson and Haggerty, 2006; Graham and Wood, 2003; Jones, 2000), these approaches have largely focused on the operation of power at a macro level and positioned this perspective along a timeline of thinking about power in relation to change at a societal level (Galič et al., 2017). We seek to consider the potential for thinking beyond this scale, or indeed the positions of actors and structures themselves, instead focusing on the relations in which these are constituted. We pursue a nuanced analysis of social media sharing that avoids reductive and simplistic framing of those who disclose personal information as foolish. Taking up Deleuze’s call to attend to the relations that pre-exist the individual, we examine the way that desires and habits create conditions where sharing is demanded, whilst also being informed by and influencing broader processes by which such data has come to be profitable and exchangeable. References Baym, N.K. and boyd, D. (2012), “Socially Mediated Publicness: An Introduction”, Journal of Broadcasting & Electronic Media, Routledge, Vol. 56 No. 3, pp. 320–329. Birchall, C. (2017), Shareveillance: The Dangers of Openly Sharing and Covertly Collecting Data, University of Minnesota Press, Minneapolis. Blum-Ross, A. and Livingstone, S. (2017), “‘Sharenting,’ parent blogging, and the boundaries of the digital self”, Popular Communication, Routledge, Vol. 15 No. 2, pp. 110–125. Christensen, M. and Jansson, A. (2015), “Complicit surveillance, interveillance, and the question of cosmopolitanism: Toward a phenomenological understanding of mediatization”, New Media & Society, Vol. 7 No. 9, pp. 1473–1491. Damkjaer, M.S. (2018), “Sharenting= Good Parenting? Four Parental Approaches to Sharenting on Facebook”, Digital Parenting, The International Clearinghouse on Children, Youth and Media at NORDICOM. Deleuze, G. (1992), “Postscript on the Societies of Control”, October, Vol. 59, pp. 3–7. Deleuze, G. (1994), Difference and Repetition, translated by Patton, P., Columbia University Press, New York, NY. Ericson, R.V. and Haggerty, K.D. (2006), The New Politics of Surveillance and Visibility, University of Toronto Press, Toronto. Fuchs, C. (2012), “The Political Economy of Privacy on Facebook”, Television & New Media, Vol. 13 No. 2, pp. 139–159. Galič, M., Timan, T. and Koops, B.-J. (2017), “Bentham, Deleuze and Beyond: An Overview of Surveillance Theories from the Panopticon to Participation”, Philosophy & Technology, Springer, Vol. 30 No. 1, pp. 9–37. Graham, S. and Wood, D. (2003), “Digitizing Surveillance: Categorization, Space, Inequality”, Critical Social Policy, SAGE Publications Ltd, Vol. 23 No. 2, pp. 227–248. Jones, R. (2000), “Digital Rule: Punishment, Control and Technology”, Punishment & Society, SAGE Publications, Vol. 2 No. 1, pp. 5–22. Marwick, A.E. and Boyd, D. (2014), “Networked privacy: How teenagers negotiate context in social media”, New Media & Society, SAGE Publications, Vol. 16 No. 7, pp. 1051–1067. Miguel, C. (2016), “Visual Intimacy on Social Media: From Selfies to the Co-Construction of Intimacies Through Shared Pictures”, Social Media + Society, Vol. 2 No. 2, pp. 1–10. Van Dijck, J. (2013), The Culture of Connectivity: A Critical History of Social Media, Oxford University Press, Oxford.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".