Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680406
Title: Examining the structures and practices for knowledge production within Galaxy Zoo : an online citizen science initiative
Author: Bantawa, Bipana
ISNI:       0000 0004 5915 6317
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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Abstract:
This study examines the ways in which public participation in the production of scientific knowledge, influences the practices and expertise of the scientists in Galaxy Zoo, an online Big Data citizen science initiative. The need for citizen science in the field of Astronomy arose in response to the challenges of rapid advances in data gathering technologies, which demanded pattern recognition capabilities that were too advanced for existing computer algorithms. To address these challenges, Galaxy Zoo scientists recruited volunteers through their online website, a strategy which proved to be remarkably reliable and efficient. In doing so, they opened up the boundaries of scientific processes to the public. This shift has led to important outcomes in terms of the scientific discovery of new Astronomical objects; the creation and refining of scientific practices; and the development of new forms of expertise among key actors while they continue to pursue their scientific goals. This thesis attempts to answer the over-arching research question: How is citizen science shaping the practices and expertise of Galaxy Zoo scientists? The emergence of new practices and development of the expertise in the domain of managing citizen science projects were observed through following the work of the Galaxy Zoo scientists and in particular the Principal Investigator and the project's Technical Lead, from February 2010 to April 2013. A broadly ethnographic approach was taken, which allowed the study to be sensitive to the uncertainty and unprecedented events that characterised the development of Galaxy Zoo as a pioneering project in the field of data-intensive citizen science. Unstructured interviewing was the major source of data on the work of the PI and TL; while the communication between these participants, the broader Science Team and their inter-institutional collaborators was captured through analyses of the team emailing list, their official blog and their social media posts. The process of data analysis was informed by an initial conceptualisation of Galaxy Zoo as a knowledge production system and the concept of knowledge object (Knorr-Cetina,1999), as an unfolding epistemic entity, became a primary analytical tool. Since the direction and future of Galaxy Zoo involved addressing new challenges, the study demanded periodic recursive analysis of the conceptual framework and the knowledge objects of both Galaxy Zoo and the present examination of its development. The key findings were as follows. The involvement of public volunteers shaped the practices of the Science Team, while they pursued robust scientific outcomes. Changes included: negotiating collaborations; designing the classification tasks for the volunteers; re-examining data reduction methods and data release policies; disseminating results; creating new epistemic communities; and science communication. In addition, new kinds of expertise involved in running Galaxy Zoo were identified. The relational and adaptive aspects of expertise were seen as important. It was therefore proposed that the development of the expertise in running citizen science projects should be recognised as a domain-expertise in its own right. In Galaxy Zoo, the development of the expertise could be attributed to a combined understanding of: the design principles of doing good science; innovation in methods; and creating a dialogic space for scientists and volunteers. The empirical and theoretical implications of this study therefore lie in (i) identifying emergent practices in citizen science while prioritising scientific knowledge production and (ii) a re-examination of expertise for science in the emerging context of data-intensive science.
Supervisor: Edwards, Anne Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.680406  DOI: Not available
Keywords: Ethnographic practices ; Education ; Internet and science and learning ; Internet research ; Shaping the Internet ; Citizen Science ; Expertise ; Relational Expertise ; Knowledge Production ; Big Data Science ; Epistemic Architecture
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