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 Arnstein
developed 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 Secondary
Upper secondary
Tertiary education

Part of a global trend…
>200 million

… with many PhD students (>1%)
>2.5 million

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%
Undergraduate
33%
Masters
22%
Doctoral
10%
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%
Undergraduate
34%
Masters
39%
Doctoral
24%
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
H. (2014) The Long Tail of OpenStreetMap http://harrywood.co.uk/blog/2014/11/17/the-long-tail-of-
eetmap/

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.

The Conservation Volunteers
Analysis by Valentine Seymour, ExCiteS

Participation across projects
High engagement Low engagement
High
Skills
Low
Skills

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
definition
Data collection
Visualisation &
analysis
Action
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?

Participatory Open Science?
(cc) Martin Clavey

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

Conclusions
• Citizen science & participation – complex
story!
• Understanding the interplay of
participation inequality, educational
attainment, gender, class, professional
knowledge, and other elements is
necessary
• Participation help in clarifying the link to
open science

Participatory [Citizen] Science

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