Social shaping and the flexibility of new technologies as a lesson for project planning Eric T. Meyer discussing the work of the Oxford e-Social Science (OeSS) Node Including work by William H. Dutton, Marina Jirotka, Ralph Schroeder, Annamaria Carusi, Jenny Fry, Paul David, Matthijs den Besten, Lucy Power and other contributors Oxford Internet Institute and Oxford Computing Laboratory University of Oxford

Source: Wuchty, S., et al. (2007). The Increasing Dominance of Teams in Production of Knowledge. Science 316, The Growth of Teams

Source: Dutton (2007). Reconfiguring Access: A Framework. Presented at World Wide Science Conference, Oxford University, June.

Social Shaping Ethical Issues “From data archive to ethical labrynth” BUT, ethical issues arise: Contextual interpretation Subject – researcher relationships Anonymisation Informed consent Access Assignment of credit and rewards Digital archives can help normalise e-social science 1.Accessibility 2.Uniformity 3.Transparency 4.Avoiding repetition / enabling secondary use 5.Meeting funding requirements Source: A. Carusi and M. Jirotka. From data archive to ethical labyrinth. In Proceedings of the 3rd International e-Social Science Conference, Ann Arbor, Michigan, USA, October 2007.

Social Shaping Legal Issues Intellectual property: GeoVue, Google, and the Ordnance Survey What constitutes consent? GeoVUE Node

Social Shaping Ethical Issues “From data archive to ethical labrynth” by Carusi & Jirotka Qualidata Legal Issues Intellectual property: GeoVue, Google, and the Ordnance Survey What constitutes consent? Institutional contexts Cultures of sharing: data archives and open science GAIN: Genetic Association Information Network Meyer, E.T. (2008). Moving from small science to big science: Social and organizational impediments to large scale data sharing. In Jankowski, N. (Ed.), E-Research: Transformation in Scholarly Practice (Routledge Advances in Research Methods series). New York: Routledge.

Control Models Alcohol –Central control –Little leeway for local differences –Distribution of centrally designed software and a standard hardware setup –Formalized communication Bipolar –Distributed control –Built-in preference for local autonomy –Hodgepodge of software, hardware, support services, methods –Combination of formal and informal communication

Clinical Coordinators Technical Coordinators PIs All project personnel PMs Communication Channels

Clinical Coordinators Technical Coordinators PIs All project personnel PMs Communication Channels Regular formal meetings Infrequent use of lists Frequent informal individual to individual Frequent semi-formal use of listservs Locally only Regular telemeetings Frequent informal individual to individual Frequent semi-formal use of listservs

Selected Innovations Expert-system databases for technophobes Tablet PC based interviewing Web-based reporting on wiki Breeze meetings Development of data dictionaries Involvement in GAIN initiative and moving toward proper e-Science

Social Informatics / STIN Approach Identify relevant populations Identify core interactor groups Identify incentives Identify excluded actors and undesired interactions Identify existing communication forums Identify resource flows Identify system choice points Map choice points to socio-technical characteristics (Kling et al., 2003, p. 57; Meyer 2006, 2007)

So what? How does this help? Socio-technical issues are often just as thorny as the technical challenges, but are often ignored and can be the difference between success and failure Flexibility in Planning and Design –Unintended consequences / unintended uses Who are the Users? Distribution of Expertise Ownership, IPR, and Openness Privacy and Confidentiality Nature and Extent of Transformations

Oxford e-Social Science (OeSS) Node of Oxford Internet Institute University of Oxford Eric T. Meyer Research Fellow Oxford e-Social Science Project