Common conceptualisations of participation assume high-level participation is good and low-level participation is bad. However, examining participation in terms of high and low levels of knowledge and engagement reveals different types of value in each case.
The spectrum of citizen science activities means some are suitable for people who have education and knowledge equivalent to PhD level, while some are aimed at non-literate participants. There are also activities suitable for micro-engagement, and others requiring deep engagement over time.
Issues of power, exploitation and commitment to engagement need to be explored for each citizen science project, as called for by the ECSA Ten Principles of Citizen Science, in response to the need for a more nuanced view that allows different activities to emerge
‘Citizen Science as Participatory Science‘ is one of the most popular posts that I have published here. The post is the core section of a chapter that was published in 2013 (the post itself was written in 2011). For the first European Citizen Science Association conference I was asked to give a keynote on the second day of the conference, which I have titled ‘Participatory Citizen Science‘, to match the overall theme of the conference, which is ‘Citizen Science – Innovation in Open Science, Society and Policy’. The abstract of the talk:
In the inaugural ECSA conference, we are exploring the intersection of innovation, open science, policy and society and the ways in which we can established new collaborations for a common good. The terms participation and inclusion are especially important if we want to fulfil the high expectations from citizen science, as a harbinger of open science. In the talk, the conditions for participatory citizen science will be explored – the potential audience of different areas and activities of citizen science, and the theoretical frameworks, methodologies and techniques that can be used to make citizen science more participatory. The challenges of participation include designing projects and activities that fit with participants’ daily life and practices, their interests, skills, as well as the resources that they have, self-believes and more. Using lessons from EU FP7 projects such as EveryAware, Citizen Cyberlab, and UK EPSRC projects Extreme Citizen Science, and Street Mobility, the boundaries of participatory citizen science will be charted.
As always, there is a gap between the abstract and the talk itself – as I started exploring the issues of participatory citizen science, some questions about the nature of participation came up, and I was trying to discuss them. Here are the slides:
After opening with acknowledgement to the people who work with us (and funded us), the talk turn the core issue – the term participation.
Type ‘participation’ into Google Scholar, and the top paper, with over 11,000 citations, is Sherry Rubin Arnstein’s ‘A ladder of citizen participation’. In her ladder, Sherry offered 8 levels of participation – from manipulation to citizen control. Her focus was on political power and the ability of the people who are impacted by the decisions to participate and influence them. Knowingly simplified, the ladder focus on political power relationships, and it might be this simple presentation and structure that explains its lasting influence.
Since its emergence, other researchers developed versions of participation ladders – for example Wiedmann and Femers (1993), here from a talk I gave in 2011:
These ladders come with baggage: a strong value judgement that the top is good, and the bottom is minimal (in the version above) or worse (in Arnstein’s version).The WeGovNow! Projectis part of the range of ongoing activities of using digital tools to increase participation and move between rungs in these concept of participation, with an inherent assumption about the importance of high engagement.
At the beginning of 2011, I found myself creating a ladder of my own. Influenced by the ladders that I learned from, the ‘levels of citizen science’ make an implicit value judgement in which ‘extreme’ at the top is better than crowdsourcing. However, the more I’ve learned about citizen science, and had time to reflect on what participation mean and who should participate and how, I feel that this strong value judgement is wrong and a simple ladder can’t capture the nature of participation in Citizen Science.
There are two characteristics that demonstrate the complexity of participation particularly well: the levels of education of participants in citizen science activities, and the way participation inequality (AKA 90-9-1 rule) shape the time and effort investment of participants in citizen science activities.
We can look at them in turns, by examining citizen science projects against the general population. We start with levels of education – Across the EU28 countries, we are now approaching 27% of the population with tertiary education (university). There is wide variability, with the UK at 37.6%, France at 30.4%, Germany 23.8%, Italy 15.5%, and Romania 15%. This is part of a global trend – with about 200 million students studying in tertiary education across the world, of which about 2.5 million (about 1.25%) studying to a doctoral level.
However, if we look at citizen science project, we see a different picture: in OpenStreetMap, 78% of participants hold tertiary education, with 8% holding doctoral level degrees. In Galaxy Zoo, 65% of participants with tertiary education and 10% with doctoral level degrees. In Transcribe Bentham (TB), 97% of participants have tertiary education and 24% hold doctoral level degrees. What we see here is much more participation with people with higher degrees – well above their expected rate in the general population.
The second aspect, Participation inequality, have been observed in OpenStreetMap volunteer mapping activities, iSpot – in both the community of those who capture information and those that help classify the species, and even in an offline conservation volunteering activities of the Trust for Conservation Volunteers. In short, it is very persistent aspect of citizen science activities.
For the sake of the analysis, lets think of look at citizen science projects that require high skills from participants and significant engagement (like TB), those that require high skills but not necessarily a demanding participation (as many Zooniverse project do), and then the low skills/high engagement project (e.g. our work with non-literate groups), and finally low skills/low engagement projects. There are clear benefits for participation in each and every block of this classification:
high skills/high engagement: These provide provide a way to include highly valuable effort with the participants acting as virtual research assistants. There is a significant time investment by them, and opportunities for deeper engagement (writing papers, analysis)
high skills/low engagement: The high skills might contribute to data quality, and allow the use of disciplinary jargon, with opportunities for lighter or deeper engagement to match time/effort constraints
low skills/high engagement: Such activities are providing an opportunity for education, awareness raising, increased science capital, and other skills. They require support and facilitation but can show high potential for inclusiveness.
low skills/low engagement: Here we have an opportunity for active engagement with science with limited effort, there is also a potential for family/Cross-generational activities, and outreach to marginalised groups (as OPen Air Laboratories done)
In short – in each type of project, there are important societal benefits for participation, and it’s not only the ‘full inclusion at the deep level’ that we should focus on.
Interestingly, across these projects and levels, people are motivated by science as a joint human activity of creating knowledge that is shared.
So what can we say about participation in citizen science – well, it’s complex. There are cases where the effort is exploited, and we should guard against that, but outside these cases, the rest is much more complex picture.
The talk move on to suggest a model of allowing people to adjust their participation in citizen science through an ‘escalator’ that we are aiming to conceptually develop in DITOs.
Finally, with this understanding of participation, we can understand better the link to open science, open access and the need of participants to potentially analyse the information.
The post focuses on the participatory aspect of different Citizen Science modes:
Against the technical, social and cultural aspects of citizen science, we offer a framework that classifies the level of participation and engagement of participants in citizen science activity. While there is some similarity between Arnstein’s (1969) ‘ladder of participation’ and this framework, there is also a significant difference. The main thrust in creating a spectrum of participation is to highlight the power relationships that exist within social processes such as urban planning or in participatory GIS use in decision making (Sieber 2006). In citizen science, the relationship exists in the form of the gap between professional scientists and the wider public. This is especially true in environmental decision making where there are major gaps between the public’s and the scientists’ perceptions of each other (Irwin 1995).
In the case of citizen science, the relationships are more complex, as many of the participants respect and appreciate the knowledge of the professional scientists who are leading the project and can explain how a specific piece of work fits within the wider scientific body of work. At the same time, as volunteers build their own knowledge through engagement in the project, using the resources that are available on the Web and through the specific project to improve their own understanding, they are more likely to suggest questions and move up the ladder of participation. In some cases, the participants would want to volunteer in a passive way, as is the case with volunteered computing, without full understanding of the project as a way to engage and contribute to a scientific study. An example of this is the many thousands of people who volunteered to the Climateprediction.net project, where their computers were used to run global climate models. Many would like to feel that they are engaged in one of the major scientific issues of the day, but would not necessarily want to fully understand the science behind it.
Therefore, unlike Arnstein’s ladder, there shouldn’t be a strong value judgement on the position that a specific project takes. At the same time, there are likely benefits in terms of participants’ engagement and involvement in the project to try to move to the highest level that is suitable for the specific project. Thus, we should see this framework as a typology that focuses on the level of participation.
At the most basic level, participation is limited to the provision of resources, and the cognitive engagement is minimal. Volunteered computing relies on many participants that are engaged at this level and, following Howe (2006), this can be termed ‘crowdsourcing’. In participatory sensing, the implementation of a similar level of engagement will have participants asked to carry sensors around and bring them back to the experiment organiser. The advantage of this approach, from the perspective of scientific framing, is that, as long as the characteristics of the instrumentation are known (e.g. the accuracy of a GPS receiver), the experiment is controlled to some extent, and some assumptions about the quality of the information can be used. At the same time, running projects at the crowdsourcing level means that, despite the willingness of the participants to engage with a scientific project, their most valuable input – their cognitive ability – is wasted.
The second level is ‘distributed intelligence’ in which the cognitive ability of the participants is the resource that is being used. Galaxy Zoo and many of the ‘classic’ citizen science projects are working at this level. The participants are asked to take some basic training, and then collect data or carry out a simple interpretation activity. Usually, the training activity includes a test that provides the scientists with an indication of the quality of the work that the participant can carry out. With this type of engagement, there is a need to be aware of questions that volunteers will raise while working on the project and how to support their learning beyond the initial training.
The next level, which is especially relevant in ‘community science’ is a level of participation in which the problem definition is set by the participants and, in consultation with scientists and experts, a data collection method is devised. The participants are then engaged in data collection, but require the assistance of the experts in analysing and interpreting the results. This method is common in environmental justice cases, and goes towards Irwin’s (1995) call to have science that matches the needs of citizens. However, participatory science can occur in other types of projects and activities – especially when considering the volunteers who become experts in the data collection and analysis through their engagement. In such cases, the participants can suggest new research questions that can be explored with the data they have collected. The participants are not involved in detailed analysis of the results of their effort – perhaps because of the level of knowledge that is required to infer scientific conclusions from the data.
Finally, collaborative science is a completely integrated activity, as it is in parts of astronomy where professional and non-professional scientists are involved in deciding on which scientific problems to work and the nature of the data collection so it is valid and answers the needs of scientific protocols while matching the motivations and interests of the participants. The participants can choose their level of engagement and can be potentially involved in the analysis and publication or utilisation of results. This form of citizen science can be termed ‘extreme citizen science’ and requires the scientists to act as facilitators, in addition to their role as experts. This mode of science also opens the possibility of citizen science without professional scientists, in which the whole process is carried out by the participants to achieve a specific goal.
This typology of participation can be used across the range of citizen science activities, and one project should not be classified only in one category. For example, in volunteer computing projects most of the participants will be at the bottom level, while participants that become committed to the project might move to the second level and assist other volunteers when they encounter technical problems. Highly committed participants might move to a higher level and communicate with the scientist who coordinates the project to discuss the results of the analysis and suggest new research directions.