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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.

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

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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