I coined the term “bounded crowdsourcing” a couple years back to distinguish the approach from other methodologies for information collection. As tends to happen, some Muggles (in the humanitarian community) ridiculed the term. They freaked out about the semantics instead of trying to understand the under-lying concept. It’s not their fault though, they’ve never been to Hogwarts and have never taken Crowdsourcery 101 (joke!).
Open crowdsourcing or “unbounded crowdsourcing” refers to the collection of information with no intentional constraints. Anyone who hears about an effort to crowdsource information can participate. This definition is inline with the original description put forward by Jeff Howe: outsourcing a task to a generally large group of people in the form of an open call.
In contrast, the point of “bounded crowdsourcing” is to start with a small number of trusted individuals and to have these individuals invite say 3 additional individuals to join the project–individuals who they fully trust and can vouch for. After joining and working on the project, these individuals in turn invite 3 additional people they fully trust. And so on and so forth at an exponential rate if desired. Just like crowdsourcing is nothing new in the field of statistics, neither is “bounded crowdsourcing”; it’s analog being snowball sampling.
In snowball sampling, a number of individuals are identified who meet certain criteria but unlike purposive sampling they are asked to recommend others who also meet this same criteria—thus expanding the network of participants. Although these “bounded” methods are unlikely to produce representative samples, they are more likely to produce trustworthy information. In addition, there are times when it may be the best—or indeed only—method available. Incidentally, a recent study that analyzed various field research methodologies for conflict environments concluded that snowball sampling was the most effective method (Cohen and Arieli 2011).
I introduced the concept of bounded crowdsourcing to the field of crisis mapping in response to concerns over the reliability of crowd sourced information. One excellent real world case study of bounded crowdsourcing for crisis response is this remarkable example from Kyrgyzstan. The “boundary” in bounded crowd-sourcing is dynamic and can grow exponentially very quickly. Participants may not all know each other (just like in open crowdsourcing) so in some ways they become a crowd but one bounded by an invite-only criteria.
I have since recommended this approach to several groups using the Ushahidi platform, like the #OWS movement. The statistical method known as snowball sampling is decades old. So I’m not introducing a new technique, simply applying a conventional approach from statistics to the field of crisis mapping and calling it bounded to distinguish the methodology from regular crowdsourcing efforts. What is different and exciting about combining snowball sampling with crowd-sourcing is that a far larger group can be sampled, a lot more quickly and also more cost-effectively given today’s real-time, free social networking platforms.