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The platform effect in social sciences Dominique Boullier Directeur de l’UMS CNRS 2809 Lutin 1st European UserLab Conference.

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Presentation on theme: "The platform effect in social sciences Dominique Boullier Directeur de l’UMS CNRS 2809 Lutin 1st European UserLab Conference."— Presentation transcript:

1 http://www.lutin.utc.fr

2 The platform effect in social sciences Dominique Boullier Directeur de l’UMS CNRS 2809 Lutin 1st European UserLab Conference Paris La Villette November 24th 2004

3 Social sciences and applied social sciences ErgonomicsMarketing Uses studies Semiotics Audience InfoCom Sciences Cognitive psychology Sociology Anthropology Economics Linguistics

4 Applied social sciences and users’ properties Applied academic fields ErgonomicsUses studies Marketing Semiotics Users dimensions OperatorClientReceiver Objects dimensions ProductServiceContent Relevant competence ManipulationAppropriationInterpretation

5 Methodologies Academic fieldCognitive psychology Sociology Anthropology Linguistics MethodologyExperimentalNaturalisticModelling

6 The pressure of computer science Applied academic fields ErgonomicsUses, Marketing Semiotics Computer science fields Man machine interfaces Computer Supported Cooperative Work, Consumer Relationship Management, User Modelling Computational linguistics

7 « Give me a lab and I shall raise the world » Sciences: confined labs, equiped, severed from society Social sciences: authors (rather than labs) involved in social debates, instable, non equiped A new era: social sciences go technical (with a little help from computer science)! But keep open to society

8 A new transdisciplinary challenge The user lab as a tool for focusing the cooperation Equipment as an « epistemic catalyst » ! Combination of methods for every project Following: The Web CSTI project as a demonstrator

9 Goals Science and technology diffusion as a field How the institutional strategies get applied on Web sites (editorial choices vs uses): analysis and strategy recommandations Validate the combination of methods: –mass data vs individual behavior –Automated and quantitative collection of data vs qualitative observation –Offer vs demand (structure and properties of sites vs tracking of web trails and uses)

10 Four workpackages, four methods 1- Topology of Web sites in the field (Tarent: dynamic map of agregates (Kleinberg), hubs, autorities) Costech 2- Web Trails analysis (In/out,inner trails ) LIP6 Combining data from topologies and mass tracking of trails

11 3- Semiotic features of Web sites: traditions indicators (Manovich), emerging conventions. Scanning of screens with specific software for semiotic categorization (in progress) Costech 4- Eye-tracking of web pages trails: cognitive load, user behavior modelling Connecting tools for describing sites and eye-tracking: offer/demand relationship Four workpackages, four methods


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