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DISTRACT SIGNED

The Political Economy of Distraction in Digitized Denmark

Total Cost €

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EC-Contrib. €

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Partnership

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 DISTRACT project word cloud

Explore the words cloud of the DISTRACT project. It provides you a very rough idea of what is the project "DISTRACT" about.

rarr    data    scarce    material    investigation    acquiring    anthropology    unseen    social    combination    distraction    tech    dimension    retention    education    captured    differentiate    natural    homogeneous    mental    urgency    alternative    human    interviews    communities    country    techniques    resource    databases    digitized    models    workplace    economics    regulation    bridging    pressing    societal    sequence    sociology    deflected    hypotheses    science    trace    layers    public    predictive    world    tools    scraping    deflection    quali    ideal    solid    analytically    interdisciplinary    national    linked    combines    scholars    off    business    environments    analysed    collected    quantitative    discourse    machine    qualitative    technologies    empirically    web    subject    politics    competition    denmark    experiments    distinguish    departs    finite    capturing    site    alluring    ethnographic    semi    retained    supervised    population    economy    learning    structured    explore    distractions    manipulated    managed    combining    political    unmet    distract    age    smartphones    grid    scientific    psychology    components    laymen   

Project "DISTRACT" data sheet

The following table provides information about the project.

Coordinator
KOBENHAVNS UNIVERSITET 

Organization address
address: NORREGADE 10
city: KOBENHAVN
postcode: 1165
website: www.ku.dk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Denmark [DK]
 Total cost 2˙499˙315 €
 EC max contribution 2˙499˙315 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-ADG
 Funding Scheme ERC-ADG
 Starting year 2020
 Duration (year-month-day) from 2020-01-01   to  2024-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KOBENHAVNS UNIVERSITET DK (KOBENHAVN) coordinator 2˙476˙790.00
2    DANMARKS TEKNISKE UNIVERSITET DK (KGS LYNGBY) participant 22˙525.00

Map

 Project objective

Bridging anthropology, sociology, economics, psychology, political science, and data science, DISTRACT combines advanced data science tools and established social science analysis to explore a pressing challenge: the ever more alluring distractions of human attention in the age of smartphones and other digitized technologies. DISTRACT departs from five linked hypotheses: 1) The attention is commonly (by scholars and laymen) seen as finite; ⇒ (2) As such, it is a scarce resource that is subject to competition and regulation; ⇒ 3) This is not new but it is acquiring unseen urgency in the current data economy; ⇒ 4) An interdisciplinary social data science approach allows for solid and novel investigation of this unmet scientific and societal need; and ⇒ 5) As the world’s most digitized country (and homogeneous population and state-of-the-art public databases), Denmark is an ideal site to study this political economy of distraction. Combining qualitative and quantitative data from four case studies, DISTRACT thus aims to trace and analyse the mental, social and material techniques by which attention is captured, retained and deflected in digitized Denmark. Analytically, we distinguish between three layers in which attention is managed and manipulated: a “mental”, “social” and “material” dimension. We also differentiate between three components of given attention/distraction sequence: the ‘”capturing”, “retention” and “deflection” phase. Empirically, case-studies shall be carried out of (a) national politics, (b) the tech business, (c) “off-the-grid” alternative communities, and (d) education and workplace environments. Data shall be collected, integrated and analysed via a combination of 1) qualitative methods, including ethnographic fieldwork and semi-structured interviews and discourse analysis; (2) quantitative methods, including natural experiments and predictive models; and (3) quali-quantitative methods including web scraping and supervised machine learning.

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The information about "DISTRACT" are provided by the European Opendata Portal: CORDIS opendata.

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