Adam* takes issue with Zooniverse, a truly impressive collection of citizen science projects ranging from its namesake Galaxy Zoo to new projects in not only astronomy but climate change, biology, even humanities. He argues that Zooniverse participants’ activities don’t count as ‘science’, but are just ‘data crunching, plain and simple’. He says such crunching is ‘what happens before the science starts’ (his emphasis) and that ‘looking at these images, clicking, sorting, categorising, isn’t the science. The science is in the interpretation that happens afterwards.’
What happened next was… I went cross-country skiing. While I was out there, huffing and puffing up uphills, rattling down icy downhills and mulling it over, Justin Starr posted a response that said several things I was planning to say, including that we should take Adam at his word that he not mean to be patronizing to Zooniverse participants and that his error lies in his definition of science.
Also while I was out skiing, people left comments on Adam’s post, many of them noting quite rightly that actually there are opportunities in Zooniverse projects to go beyond data crunching and interpret data.
The question becomes, then, what can I add?
First, I think that whether or not Adam intended to be patronizing towards Zooniverse participants, his post… well, it was. Adam was (as I am) writing from a position of privilege. We are professional scientists with the luxury of being identified by the world as such. When one of us says that while Zooniverse participants should be invited to do science, what they are doing doesn’t count as science, it is, by the very definition, patronizing.
Second, if anyone gets to decide what ‘science’ is and whether Zooniverse participants are doing it, it’s not Adam Stevens. And it’s not me, either. Who is it, then? It is, first and foremost, the participants themselves, and second, those who run Zooniverse and its projects. Why does it matter if anyone else doesn’t think what they’re doing is ‘science’? Who does it hurt? No-one. This is not to say that Adam’ suggestions for more opportunities to understand the meaning of and interpret data are not good ones. But to preface those suggestions by calling the current projects ‘not science’ and to call them instead ‘patronizing slave labour’ is not helpful to anyone.
Digression: One could argue that even if Adam’s suggestions were adopted, interpretations made by participants might not be used by the the professional scientists who are partnering on the project and who are presumably interpreting the data ‘for real’. In other words, even if you provide widgets to help participants understand the meaning of the data they are collecting and processing, they may still not be doing ‘science’ a la Adam.
Third and finally, I’d like to add some insights that I’ve gained from developing my own citizen science project. I was recently awarded a grant from the National Science Foundation to pilot a new framework for citizen science called ‘BioTrails’. In the process of writing the proposal, I learned a lot from my reading and from my collaborators and evaluators.
One of the most important things I learned was about the many different ways that people practice citizen science and how those different practices translate to learning and other positive outcomes for participants. Here’s a quick run-down. As you’ll see, this runs somewhat counter to what Adam wrote in his post about what ‘citizen science’ is and should be.
In 2009, the Center for Advancement of Informal Science Education (CAISE) published a study of existing citizen science projects and programs. The report identifies three project types (excerpted below exactly as listed in the report summary):
- Contributory projects designed by scientists, with participants involved primarily in collecting samples and recording data
- Collaborative projects in which the public is also involved in analyzing data, refining project design, and disseminating findings
- Co-created projects are designed by scientists and members of the public working together, and at least some of the public participants are involved in all aspects of the work
The important take-home here for the purposes of this blog post is that all three of these kinds of projects ‘count’ as citizen science. It’s true that each type of project can deliver different kinds of experiences for participants, and result in different learning outcomes. But they count!
So, what are some of these ‘learning outcomes’? In Surrounded by Science: Learning Science in Informal Environments Marilyn Fenichel and Heidi Schweingruber identify six strands of informal science learning:
- Sparking Interest and Excitement
- Understanding Scientific Content and Knowledge
- Engaging in Scientific Reasoning
- Reflecting on Science
- Using the Tools and Language of Science
- Identifying with the Scientific Enterprise
Looking at this list, it is obvious that whether or not Zooniverse participants interpret the data they collect or process, they can benefit from several of these kinds of learning. There is a lot more to say – and a lot more that has been said – about citizen science and how it can lead to learning. My point here is simply to highlight the breadth of what may ‘count’ as ‘citizen science’ and some of the many learning outcomes that can result, and to applaud the Zooniverse for their very significant contributions to the field and, most importantly, to their participants.
*On initial posting, I had ‘Dr. Stevens’ throughout, but he kindly informed me he hasn’t received his PhD yet, hence ‘Adam’.