Participatory [Citizen] Science
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Transcript of Participatory [Citizen] Science
Participatory [Citizen] Science
Muki Haklay
Extreme Citizen Science (ExCiteS) research group, UCL
@mhaklay @UCL_ExCiteS
Acknowledgement
This talk would not be possible without the
generosity of the many people and
communities that we have worked with
over the years…
Acknowledgement
… and the funders and project partners that we’ve
worked with (and will work with in the future)
Outline
• What do we mean by participation?
• A closer look at demographics and
participation inequality in citizen science
• Outcomes: different modes of
participations, implications to open
science agenda
A Ladder of Citizen Participation
• In 1969, based on her experience at the US department for Housing, Education and Welfare (HUD), Sherry Rubin Arnsteindeveloped a typology of citizen participation –Arnstein’s ladder
Arnstein, S.R., 1969. A ladder of citizen participation. Journal of the American Institute of
planners, 35(4), pp.216-224.
Participation Ladders
Public Right to Object
Restricted Participation
Public Right to Know
Informing the Public
PP in the final decision
Public Participation (PP) in
defining interests, actors and agenda
PP in assessing Risks and
Recommending Solutions
Wiedemann, P.M. and Femers, S., 1993. Public participation in waste management decision
making: Analysis and management of conflicts. Journal of Hazardous Materials, 33(3), pp.355-368.
Participation in Citizen Science
Level 4 ‘Extreme’
• Participatory Science – problem definition, data collection and analysis
Level 3 ‘Participatory science’
• Participation in problem definition and data collection
Level 2 ‘Distributed Intelligence’
• Citizens as basic interpreters
Level 1 ‘Crowdsourcing’
• Citizens as sensors
Haklay, M., 2013. Citizen science and volunteered geographic information: Overview and
typology of participation. In Crowdsourcing geographic knowledge (pp. 105-122).
Level 4 ‘Extreme’
• Participatory Science – problem definition, data collection and analysis
Level 3 ‘Participatory science’
• Participation in problem definition and data collection
Level 2 ‘Distributed Intelligence’
• Citizens as basic interpreters
Level 1 ‘Crowdsourcing’
• Citizens as sensors
Participation in Citizen Science
Source: BioScience 58(3) p. 192
Hanny van Arkel. “The Dutch schoolteacher and Queen
admirer who discovered Hanny’s Voorwerp”.
Not so simple!
• Projects at the ‘bottom’
demonstrate deep
engagement:
– Hanny van Arkel,
– Green Peas,
– Teams in volunteer
computing projects…
• Two characteristics, in
particular:
– Educational attainment
– Participation
Educational attainment
• Among the general
population of EU 28, the
education attainment is
27% tertiary education
(university).
• Variability: UK 37.6%,
France 30.4%, Germany
23.8%, Italy 15.5%,
Romania 15%
27%
46%
27%
Education Attainment EU 28 (2015)
Up to Lower SecondaryUpper secondaryTertiary education
OpenStreetMap (2010)
High School or lower
(5%)
Some College(17%)
Undergraduate(49%)
Masters (21%)
Doctoral (8%)
Budhathoki, N.R. and Haythornthwaite, C., 2013. Motivation for open collaboration crowd
and community models and the case of OpenStreetMap. American Behavioral Scientist, 57(5),
pp.548-575.
Galaxy Zoo (2013)
High School or unknown
35%
Undergraduate33%
Masters22%
Doctoral10%
Raddick, M.J., Bracey, G., Gay, P.L., Lintott, C.J., Cardamone, C., Murray, P., Schawinski, K.,
Szalay, A.S. and Vandenberg, J., 2013. Galaxy Zoo: Motivations of citizen scientists. arXiv
preprint arXiv:1303.6886.
Transcribe Bentham (2012)
High School or unknown
3%
Undergraduate34%
Masters39%
Doctoral24%
Causer, T, and Wallace, V., 2012. Building a volunteer community: results and findings from
Transcribe Bentham. Digital Humanities Quarterly , 6
Participation Inequality (90-9-1)
Nielsen, J., 2006. Participation inequality: lurkers vs. contributors in internet
communities. Jakob Nielsen's Alertbox.
OpenStreetMap (2014)
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
Wood, H. (2014) The Long Tail of OpenStreetMap http://harrywood.co.uk/blog/2014/11/17/the-long-tail-of-
openstreetmap/
iSpot – observers & id’ers• iSpot provide two demonstration: in the effort of
observations, and in the identification (c. 200,000 participants)
Silvertown, J., Harvey, M., Greenwood, R., Dodd, M., Rosewell, J., Rebelo, T., Ansine, J. and
McConway, K., 2015. Crowdsourcing the identification of organisms: A case-study of
iSpot. ZooKeys, (480), p.125.
High engagement Low engagement
High Skills
• Highly valuable effort: research assistants
• Significant time investment
• Opportunities for deeper engagement (writing papers, analysis)
• Skills might contribute to data quality
• Possible use of disciplinary jargon
• Opportunities for lighter or deeper engagement to match time/effort constraints
Low Skills
• Providing an opportunity for education, awareness raising, increased science capital, other skills
• Require support and facilitation
• Opportunity for active engagement with science with limited effort
• Family/cross-generational potential
• Outreach to marginalised groups (OPen Air Laboratories)
Complex participation
• Not ‘more control = good / less control = bad’
• Participation of the privileged (scientific
0.1%?) for the common good: public scientific
knowledge
• Outreach and engagement with marginalised
groups provide skills, opportunities, science
capital
• Variable depth of participation address
lifestyles, care responsibilities, constraints
Complex participation
• Participation ∩ Education attainment =
range of skills and levels of engagement.
We know that the relationships are not
simple
• From ‘one time’ to ‘dedicated expert’,
with different formal titles, authority,
knowledge and patterns of activity
Risks to participation
Exceptions:
• Level of control by
project owners
• Purpose of the
project
• Duty of care for
participants
• Can be exploitative
Arnstein & Citizen Science
• Citizen control, a-la Arnstein is needed in
some cases: Civic Science
• Knowingly delegating power to scientists
can be a preferred option
• Partnership and
co-creation, even
informing (‘I’m glad
someone is doing it’) are
valuable
Problem
definitionData collection
Visualisation &
analysisAction
Classification
& basic analysis
Basic School
High School
University/
College
Postgraduate
PhD
Literacy
Extending citizen science
Participatory Citizen Science
• How can we find routes to make citizen science participatory across the range activities?
• What is the engine for the escalator? Is there an engine?
• Do we want ‘nudges’? Behaviour change?
• What are the social and individual costs of change? Who pays?
Citizen Science & Open Science
• Participants are well educated & contribution to science is known to be a core motivator
• They Provide free labour and/or resources, and many want to see outputs used openly
• Have the right to read about the research they’ve done
• Open access publications are necessary to keep motivation & feedback
• Participants can also analyse the data and might have their own analysis, visualisations and conclusions. Open source tools make this possible.
Citizen Science & Scientific Publication
• Strong support
for Open
Access
• Creative
solutions to
open access to
data &
publications
emerge