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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.

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

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

Leaflet | Map data © OpenStreetMap contributors, CC-BY-SA, Imagery © Mapbox

 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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