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.

WeGovNow!

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

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%

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.

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)

Motivations

(cc) Marta Soukup

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

DITOs ‘escalator’

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