Thursday, January 17, 2013

The Levels of Citizen Science Involvement - Part 2

Monday night I posted a well-received article on the types and levels of citizen science projects.  It my highest rated post ever (thank you everyone!).  It also let me combine much of the citizen science data I've collected over the last few years.  So I want to go another level today and talk about how this information can help us.  We talked about theory...now let's talk practice.

After all, expanding and improving the practice  of citizen science is what the California Academy of Sciences meeting was all about.

One of the questions I'm most often asked is how scientists can get more people more involved in their projects.  While this can have many answers, much comes down to Motivation.  So this is the perfect test case to see what the activity level models can tell us about ways to improve citizen science motivation and recruitment.  For example:

  • Challenge projects are often the most difficult sort of citizen science project to participate in, and can be considered Expert or Co-created depending on the model.  For these, due to the high barrier to entry, large amount of training research required, and time/effort required, tangible rewards (such as cash prizes) seem to be required.  Obviously intellectual curiosity  and civic-mindedness play a role, but keeping people involved requires the promise of more tangible rewards.  Thus the challenge leading to a prize at the end.  Examples of this include the Ansari X-Prizes and various Innocentive initiatives.  Both require expert levels of participation and involve much creation (co-creation) by participants.  In return cash rewards (or the potential for such rewards) keep people motivated.
  • At the Medium or Collaborative Level much time and effort is required, so designers should put much time and effort into keeping participants motivated.  Tangible rewards often work for these groups and may be very useful.  But other less tangible rewards can be successfully used too.  One of the most powerful are intellectual rewards (such as extensive training in the science or public recognition of important discoveries)  These are highly prized and can be given often without significant cost, and not only keep the person motivated but also makes them a more productive citizen scientist.  They understand the project more, provide higher-quality data, have more insightful analysis, and provide larger data sets.  Good examples of this are the Sungrazer comet detection project and the Citizen Weather Observing Program.
  • At the Novice and Contributory Level people don't require much initial training (in most cases) or require extensive time commitments.  For example, people only need enough training to collect water samples from local streams, or count wildlife in their backyards.  Projects are certainly improved when people stay for a long time or receive extensive training that allows high-quality data, but it's usually not required.  A project can still be successful without large motivation levels.  So successful participation requires stoking immediate interest for recruitment purposes and then just enough other benefits (usually intangible)  for them to continue participating.  This can include civic-mindedness (wanting to help the world) or fostering a sense of dependence (showing how the science will benefit them in their real lives). 
  • Distributed computing takes the least amount of effort for citizen scientists to participate in and it doesn't even register on the CAISE model.  But these projects also require a different type of  motivation...it seems just providing feedback and keeping the project in the public's eye is good enough.  So focusing on initial recruitment and getting the program on a person's computer is a project designers most important taks.  Once that is done the program can just keep running.  There isn't much need to continue adding rewards or other motivational techniques to keep the research moving forward.
Now let's dive a bit deeper.  Pulling from my previous research on keys to successful citizen science projects, a few important factors stand out for engaging users.

First is the need to focus on "creating a community" and "educating participants" about the science as keys to success in ecology-based projects.   These are not monetary rewards, but are non-monetary incentives to recruit and motivate participants.  People want to learn more about their projects and the world around them, and appreciate the education provided by joining.  They also find a sense of community and camaraderie that both provides training and makes the work fun.

All this fits perfectly with the Contributory type of participation common with most ecology-based projects.  Grand monetary rewards or extensive public recognition are not required...instead personal recognition (sincere thank yous) and basic science training just as powerful.  Understanding this helps designers tailor projects to their audience.

Second, we also see this with distributed computing projects as well.  The most important success factor is providing feedback.  Not even public recognition, just feedback about the project's accomplishments and confirmation that each person's involvement is worth while.  This is a very low-level of engagement but is enough to meet the needs of people participating with a minimal level of ongoing activity.

Next, let's go back to the highest level of participation, Co-created or Expert levels of activity.  At this level we ask for large commitments of time/energy and project designers must work that much harder to keep people involved.  These are the big projects that require big solutions.  They also seem to require large benefits and tangible rewards...thus the need for cash-based challenges to promote participation.  We've seen this is not required for other levels of citizen science activity, but at the Co-created level we need a greater push to keep large numbers of people involved.  Also, remember that they are the creators and want to receive the credit, both tangible and intangible.  Just like the project designers do.  So it's just fair to provide it.  After all, these biggest challenges also provide the best opportunity for huge advances to the scientific field.  So it's often worth it.

Finally, while we've talked a good game above, can we really be sure that this model works?  Are those success factors and motivation techniques really correlated to the various types/levels of citizen science activity?  Let's find out.  I have a lot of data on existing projects and their levels of activity, so let's run some numbers and see if the model holds up.  So come back soon and we'll walk through everything together.



PREVIOUS POSTS IN THIS SERIES:
  • The Levels of Citizen Science Involvement - Part I (Comparing the Models)
  • The Levels of Citizen Science Involvement - Part II (Implementing the Models) - Today
  • The Levels of Citizen Science Involvement - Part III (Testing the Models) - Coming Soon!

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