Crowdsourcing citizen science: exploring the tensions between paid professionals and users

This paper explores the relationship between paid labour and unpaid users within the Zooniverse, a crowdsourced citizen science platform. The platform brings together a crowd of users to categorise data for use in scientific projects. It was initially established by a small group of academics for a single astronomy project, but has now grown into a multi-project platform that has engaged over 1.3 million users so far. The growth has introduced different dynamics to the platform as it has incorporated a greater number of scientists, developers, links with organisations, and funding arrangements—each bringing additional pressures and complications. The relationships between paid/professional and unpaid/citizen labour have become increasingly complicated with the rapid expansion of the Zooniverse. The paper draws on empirical data from an ongoing research project that has access to both users and paid professionals on the platform. There is the potential through growing peer-to-peer capacity that the boundaries between professional and citizen scientists can become significantly blurred. The findings of the paper, therefore, address important questions about the combinations of paid and unpaid labour, the involvement of a crowd in citizen science, and the contradictions this entails for an online platform. These are considered specifically from the viewpoint of the users and, therefore, form a new contribution to the theoretical understanding of crowdsourcing in practice.

[1]  R. Bonney,et al.  Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy , 2009 .

[2]  David De Roure,et al.  Zooniverse: observing the world's largest citizen science platform , 2014, WWW.

[3]  Peng Dai,et al.  Decision-Theoretic Control of Crowd-Sourced Workflows , 2010, AAAI.

[4]  C. Lintott,et al.  Galaxy Zoo: 'Hanny's Voorwerp', a quasar light echo? , 2009, 0906.5304.

[5]  Bruce V. Lewenstein,et al.  What does citizen science accomplish , 2004 .

[6]  Kieran Mathieson,et al.  Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior , 1991, Inf. Syst. Res..

[7]  Philip Barker,et al.  Blogs, Wikipedia, Second Life, and beyond: From Production to Produsage , 2009 .

[8]  Tim Causer,et al.  Building A Volunteer Community: Results and Findings from Transcribe Bentham , 2012, Digit. Humanit. Q..

[9]  Kevin Crowston,et al.  From Conservation to Crowdsourcing: A Typology of Citizen Science , 2011, 2011 44th Hawaii International Conference on System Sciences.

[10]  David H. Guston,et al.  Forget politicizing science. Let's democratize science! , 2004 .

[11]  Elena Paslaru Bontas Simperl,et al.  Why Won't Aliens Talk to Us? Content and Community Dynamics in Online Citizen Science , 2014, ICWSM.

[12]  Lilly Irani,et al.  Amazon Mechanical Turk , 2018, Advances in Intelligent Systems and Computing.

[13]  Georgia Bracey,et al.  Citizen Science: Status and Research Directions for the Coming Decade , 2009 .

[14]  Rolf Lidskog,et al.  Scientised citizens and democratised science. Re‐assessing the expert‐lay divide , 2008 .

[15]  C. Potter,et al.  Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions , 2014, Public understanding of science.

[16]  C. Lintott,et al.  Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey , 2013, 1308.3496.

[17]  Gabrielle Durepos Reassembling the Social: An Introduction to Actor‐Network‐Theory , 2008 .

[18]  K. Marx Capital: A Critique of Political Economy , 1867 .

[19]  Jean-Jacques Salomon Science, Technology and Democracy , 2000 .

[20]  Karim R. Lakhani,et al.  Marginality and Problem-Solving Effectiveness in Broadcast Search , 2010, Organ. Sci..

[21]  Udo Kruschwitz,et al.  Methods for Engaging and Evaluating Users of Human Computation Systems , 2013, Handbook of Human Computation.

[22]  Yann,et al.  Cognitive Capitalism , 2013 .

[23]  Alimohammad Shahri,et al.  Towards a Code of Ethics for Gamification at Enterprise , 2014, PoEM.

[24]  Nico Carpentier,et al.  The concept of participation. If they have access and interact, do they really participate? , 2011 .

[25]  Frank A. Pasquale The Black Box Society: The Secret Algorithms That Control Money and Information , 2015 .

[26]  Ashok N. Srivastava,et al.  Advances in Machine Learning and Data Mining for Astronomy , 2012 .

[27]  Marion Poetz,et al.  Crossing Domain-Specific Boundaries in Search of Innovation: Exploring the Potential of 'Pyramiding' , 2009 .

[28]  J. Hayden Cognitive Surplus: Creativity and Generosity in a Connected Age , 2011 .

[29]  Jordan Raddick,et al.  Galaxy Zoo: Morphological Classification and Citizen Science , 2011, 1104.5513.

[30]  Anna L. Cox,et al.  Eight Guidelines for Designing Virtual Citizen Science Projects , 2014, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing.

[31]  Stuart Dunn,et al.  Crowd-sourcing as a Component of Humanities Research Infrastructures , 2013 .

[32]  H. Sauermann,et al.  Crowd Science: The Organization of Scientific Research in Open Collaborative Projects , 2013 .

[33]  Lilly Irani,et al.  The cultural work of microwork , 2015, New Media Soc..

[34]  N. Baym Interpersonal Life Online , 2002 .

[35]  Justin Longo,et al.  Design principles for engaging and retaining virtual citizen scientists , 2016, Conservation biology : the journal of the Society for Conservation Biology.