Opendata, web and dolomites


The Political Economy of Distraction in Digitized Denmark

Total Cost €


EC-Contrib. €






 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.

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

Project "DISTRACT" data sheet

The following table provides information about the project.


Organization address
address: NORREGADE 10
postcode: 1165

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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KOBENHAVNS UNIVERSITET DK (KOBENHAVN) coordinator 2˙476˙790.00


 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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