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.

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

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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