Calibrating families: data behaviourism and the new algorithmic logic
Calibrating families: data behaviourism and the new algorithmic logic
Family intervention is a long-established mechanism of state control, but recent technological developments are facilitating new regulatory capacities and objectives. This paper will explore how contemporary policy interventions in the UK are converging around a technological solutionist ideology that centres family relationships as core instruments of social management. The last decade has seen a marked techno-administrative turn, with family state relationships increasingly mediated through online portals and dashboards. Over the last few years this data centric model has accelerated towards an algorithmic approach to governance through the incorporation of big data surveillance, predictive analytics and behavioural interventions to monitor and socially engineer populations. We trace the embedding of data collection frameworks into apparently conventional family intervention programs and show how this ‘datification’ was made into a core delivery tool. We also highlight how secrecy, or at the very least strategic silence, has restricted public knowledge of how and why data is being collected and used in the UK. We show how parents and children are being quantified and translated into data points to support new logics of choice manipulation, ceding unprecedented power to financiers, data analytic and social marketing companies, platform developers and big tech industries. This algorithmic calibration of children and parents is extending the regulatory powers of the state far beyond previous efforts to govern and control poor families with under explored consequences for principles of democracy and justice.
data analytics, behaviourism, algorithms, Families, governance
Edwards, Rosalind
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Gillies, Val
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Vannier Ducasse, Helene
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Edwards, Rosalind
e43912c0-f149-4457-81a9-9c4e00a4bb42
Gillies, Val
9c9bcf7c-be6d-4fce-bc64-4df1c1953db1
Vannier Ducasse, Helene
dc8d04de-9476-4fdd-92c0-f1bbb4d88939
Edwards, Rosalind, Gillies, Val and Vannier Ducasse, Helene
(2024)
Calibrating families: data behaviourism and the new algorithmic logic.
Zeitschrift für Sozialreform.
(doi:10.1515/zsr-2024-0008).
Abstract
Family intervention is a long-established mechanism of state control, but recent technological developments are facilitating new regulatory capacities and objectives. This paper will explore how contemporary policy interventions in the UK are converging around a technological solutionist ideology that centres family relationships as core instruments of social management. The last decade has seen a marked techno-administrative turn, with family state relationships increasingly mediated through online portals and dashboards. Over the last few years this data centric model has accelerated towards an algorithmic approach to governance through the incorporation of big data surveillance, predictive analytics and behavioural interventions to monitor and socially engineer populations. We trace the embedding of data collection frameworks into apparently conventional family intervention programs and show how this ‘datification’ was made into a core delivery tool. We also highlight how secrecy, or at the very least strategic silence, has restricted public knowledge of how and why data is being collected and used in the UK. We show how parents and children are being quantified and translated into data points to support new logics of choice manipulation, ceding unprecedented power to financiers, data analytic and social marketing companies, platform developers and big tech industries. This algorithmic calibration of children and parents is extending the regulatory powers of the state far beyond previous efforts to govern and control poor families with under explored consequences for principles of democracy and justice.
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Accepted/In Press date: 17 April 2024
e-pub ahead of print date: 3 September 2024
Keywords:
data analytics, behaviourism, algorithms, Families, governance
Identifiers
Local EPrints ID: 489281
URI: http://eprints.soton.ac.uk/id/eprint/489281
ISSN: 2366-0295
PURE UUID: 00036818-cc0c-4074-a178-0a5bd9faa81a
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Date deposited: 19 Apr 2024 16:33
Last modified: 18 Sep 2024 01:44
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Author:
Val Gillies
Author:
Helene Vannier Ducasse
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