I attended in Oslo Science Park the seminar on citizen participation, expertise, and knowledge sharing in cultural heritage archives and natural history institutions. The seminar dealt with different aspects, which i found interesting to keep a record about.
Bernard Schiele from University of Quebec emphasised the need to overcome the Deficit model in explaining the role of public engagement and citizen science – a knowledge gap between public being illiterate about science and scientists seen merely as the teachers about science. He opened up the high level bodies expectations recently prevalent about public engagement in science as Triple democratisation and engagement model – i) including laypersons in the phase of making science decisions while exercising democratic rights, ii) making science as a co-production involving knowledge of laypersons, as well as iii) making reports accessible to laypersons in an understandable format.
Socientize.eu White paper on Citizen Science aims to support policy makers on European, national and regional level when setting up future strategies of civic engagement in the excellence in science
- Interrelation in deficit model is paradigm asymmetric, in public engagement model symmetric.
- Interpersonal relationships in deficit model are compelling, while in engagement model collaborative.
- Interaction in deficit model positions science into an authoritative position, while in engagement model equal rights to contribute are considered.
- The conditions in deficit model between scientists and laypersons are following the dependence model regarding the latter, in engagement model autonomy of science and laypersons is practiced.
- In dependence model the behaviour of laypersons is submissive, while in engagement model the reciprocal engagement is practiced.
- The knowledge transfer changes fro deficit model one-directional transfer from science to people towards mutual knowledge transfers.
Three models for participation and public engagement in science are: 1) dialogue critical (such as exhibitions, fairs), ii) deliberative democracy (such as consensus conferences) and iii) knowledge co-production (participating together in research projects operating from different locations).
In the discussion the interesting books of social ecology were mentioned: Social Ecology and Social Change by Erik Eiglad.
Science museums so far have been presenting science already made but it should be shifted towards engaging people into science in making what happens at this moment, time and space. The changes that have to be achieved are:
- From one voice towards multiplicity of voices
- From dominant view to various views
- From presenting truth to presenting conflicts, disagreements
- From linear approach to questions and challenges, multi-faceted open science approach that involves laypersons in engagement
- From facts, results to relations between people
- From closed, stabilised, fixed, secure knowledge to presenting also tentative results, failures, aberrations, presenting unfinished knowledge and processes
New media has opened new forms of participatory public engagement that has to be:
- reciprocal regarding exchanges of knowledge between layperson and scientists
- regarding the interdependency of different society groups and scientists
- allowing local knowledge in context to impact science and technology
Dick Kasperowski from LET studio in Dept. of Theory of Science, University of Gothenburg introduced a meta-study considering citizen science papers. He divided citizen science initiatives into two types: i) Perception mode, and ii) Representation mode
However, if to look what way science museums, knowledge institutions and archives so far engage with public, the tendency of engagement is towards using the laypersons as a work labour, either in data collection, metadata tagging, transcribing, validation (having layers of validation for verification of contributed amateur data) or as sensors in pattern recognition (Perception mode). That is not a democracy in open citizen science, rather Taylorism 2.0.
Issues: The scaling up of data done with amateurs is still a problem – the data collected by them are not considered as same valuable. It has been found that when amateurs use protocols, these practices do not scale – protocols do not withstand many users. Protocols start to leak – amateurs start to do other things than expected.
What is needed – instead of mobilising human perception the interpretational cultural contributions should be requested from amateurs. Cultural contributions could allow creating values beyond tasks.
It is important to move from citizens as research object to citizens as research subject.
European citizen science association claims that people should be included also in hypothesis creation and interpretation.
Interesting examples of citizen science:
- Galaxy Zoo – classifying galaxies
- Shakespeare’s world – Transcribe handwritten documents by Shakespeare’s contemporaries and help us understand his life and times. Along the way you’ll find words that have yet to be recorded in the authoritative Oxford English Dictionary, and which will eventually be added to this important resource.
- Micropast: crowd sourcing: You can assist existing research projects with tasks that need human intelligence, such as the accurate location of artefact findspots or photographed scenes, the identification of subject matter in historic archives, the masking of photos meant for 3D modelling, or the transcription of letters and catalogues. Other tasks might require on-location contributions by members of the public, such as submitting your own photographs of particular archaeological sites or objects.
- Crowdsourcing the Bronze Age in the platform of Archeology – Archival transcription, photo masking
- Transcribe weather blog oldweather.org
Help scientists recover Arctic and worldwide weather observations made by United States ships since the mid-19th century by transcribing ships’ logs. These transcriptions will contribute to climate model projections and will improve our knowledge of past environmental conditions. Historians will use your work to track past ship movements and tell the stories of the people on board.
- Roots web family history forum
- Notes from the Nature, part of ZooUniverse – transcribe museum records
One form that citizen science is taking is citizens using the data they collect in law cases ( such as plant data, water or air data).
Example Louisiana bucket brigades
Alexandra Everleigh presented motivational issues in citizen participation projects:
Issues: small number of participants do the most work, enthusiasm drops in time, contributing very much on their own terms
The contributions are motivated by fun, wish to contribute in science, unhealthy addiction, feeling part of something bigger or part of community ( social exclusion issues)
Motivational elements used to engage people more are: competitive participatory levels, badges, race to the finish (that is discouraging if the target is far). Best motivations would be personal challenges that are motivated from own interest, demonstrating how contributions make the difference; finding own narratives; open-ended discoveries.
Christine Hine, University of Surrey, Dept. of Sociology has recently edited special issues on socio-technical systems in Science and Technology Studies 29(1)-29(4), where several public engagement projects are depicted.
- Building Knowledge Infrastructures for Empowerment: A Study of Grassroots Water Monitoring Networks in the Marcellus Shale Kirk Jalbert
This paper characterizes the activities of two nongovernmental environmental monitoring networks working to protect watersheds in the Northeast United States from the impacts of shale oil and gas extraction. The first is a grassroots coalition of advocacy groups. The second is a large network managed by academic institutions. In both cases, knowledge infrastructures were built to distribute resources and to assist members in using data to make scientific claims.
- Co-Observing the Weather, Co-Predicting the Climate: Human Factors in Building Infrastructures for Crowdsourced Data Yu-Wei Lin, Jo Bates, Paula Goodale
We found that conducting citizen science is highly emotional and experiential, but these individual experiences and feelings tend to get lost or become invisible when user-contributed data are aggregated and integrated into a big data infrastructure. While new meanings can be extracted from big data sets, the loss of individual emotional and practical elements denotes the loss of data provenance and the marginalisation of individual ef orts, motivations, and local politics, which might lead to disengaged participants, and unsustainable communities of citizen scientists. The challenges of constructing a data infrastructure for crowdsourced data therefore lie in the management of both technical and social issues which are local as well as global.