Community Science

Food for thought:  
Over the years, teachers and faculty have adopted topics in Wikipedia as a way to practice Open Pedagogy with reusable assignments.  It provides a real life experience to evaluate information and edit with new or contradictory evidence.

The pro and con arguments that apply to the crowd-sourced Wikipedia often apply to citizen science crowd-sourcing, too.  

• Honest and objective data collection can bring tremendous insight into an issue.  
• Topics can be distorted aggressively by people with an agenda.  
• Normal confirmation bias can bias the observations without the observer realizing it. 

Confirmation bias examples:

• Discounting: Counting fewer birds where fewer birds are expected.
• Overcounting: Counting more birds when the birds are noisy, hyperactive, or expectations are for more birds. 
• Propaganda: Cherry-picked, exaggerated, or faked data provided to make the results appear more supportive of a theory (such as there are fewer/more) birds than before.
• Hubris: Mis-identifying a bird because you "know" what that bird looks like, and so you don't need to look it up.

Keep in mind: 

1. Sometimes a large number of data points can give you a general idea of something that is otherwise difficult to determine.  For examples, bird populations or butterfly migration routes.
2. One hopes the sheer number of data points will minimize the effects of collection errors, but that is not always true.
3. GIGO (Garbage In-Garbage Out ): The data is only as good as the method used to collect and record the data.
4. Whenever possible, data should be confirmed with more rigorous research by authorities in the fields studied.