What is it that means one open source project takes off, while
another doesn't? There are a lot of ways to analyse this question
depending on the example at hand, but a more general study of the
"remixability" of online content has found a surprising correlation
-- there's a trade-off between originality and the chance it will
inspire new versions.
Benjamin Mako Hill
from MIT and Andrés
Monroy-Hernández from the Berkman Center for Internet and
Society at Harvard University wanted to look at a particular
dilemma -- despite "proponents of remix culture often speaking of
remixing in terms of rich ecosystems where creative works are novel
and highly generative", actual examples of it happening "can be
difficult to find", Monroy-Hernández writes on
"We suggest that each of the factors associated with
generativity will also be associated with less original forms of
"Although there is a steady stream of media being shared
freely on the web, only a tiny fraction of these projects are
remixed even once. On top of this, many remixes are not very
different from the works they are built upon. Why is some content
more attractive to remixers? Why are some projects remixed in
deeper and more transformative ways?" Examples of "peer production"
like Wikipedia and Linux are the exceptions, not the rule.
Luckily, a large dataset from an active community of remixers
was available for them to test out some hypotheses on. MIT Media
Scratch is an
online community for children that teaches them how to make simple
animations, simulations and programs, and encourages them to share
them online for others to remix.
It's extremely popular -- not only have more than three million
projects been created by more than a million users and shared by
its community, educators around the world are considering
adding it to their IT curriculums -- and its open standard gave
Hill and Monroy-Hernández a sample dataset of 536,245 projects
by 105,317 unique users taken from across the year-long period
As they write in their paper -- "The Remixing Dilemma: The
Trade-Off Between Generativity and Originality", published in
-- the two researchers used the
sample to explore several hypotheses about remixing, originality
and "generativity" (the word they use to describe how likely a work
is to inspire remixing). American
These can be grouped into two apparently paradoxical main
theories. The first is that the most generative projects are going
to be ones that are moderately complicated first-generation
projects by prominent authors, while the second is that the most
original projects are going to be ones that are moderately
complicated remixes of remixes by less famous creators. In other
words, as Monroy-Hernández writes, "we suggest that each of
the factors associated with generativity will also be
associated with less original forms of remixing".
The theoretical reasoning behind these hypotheses is
discussed in detail in the paper, referencing P Diddy's 1997
" Every Breath You Take"-sampling hit " I'll Be Missing
You" along the way, but the key thing is that the more likely
your work is going to get remixed, the less likely it's going to be
in a genuinely new and interesting way. They call this trade-off
between originality and generativity "the remixing dilemma". The
Scratch data confirms each side of the idea.
To test whether the two are linked, though, required re-testing
with a new dataset of every project on Scratch that has ever been
remixed and tracing it back to its original project. They then
created a value for what they call the "edit distance", a
combination of variables that together represent just how original
a remix is -- including the amount of code the remix shares with
the original, the number of times a project was viewed versus the
number of remixes made and the demographic breakdowns of users and
the time between remixes appearing.
The data showed the remixing dilemma held true, with originality
increasing as generativity decreased. This is a troubling
conclusion for peer production, open source projects and so on,
because it implies that truly innovative technology is the opposite
of what collaborative remixing produces.
In the blog, Monroy-Hernández writes: "
We feel that our
results raise difficult but important challenges, especially for
the designers of social media systems. For example, many social
media sites track and display user prominence with leaderboards or
lists of aggregate views. This technique may lead to increased
generativity by emphasizing and highlighting creator prominence.
That said, it may also lead to a decrease in originality of the
remixes elicited. Our results regarding the relationship of
complexity to generativity and originality of remixes suggest that
supporting increased complexity, at least for most projects, may
have fewer drawbacks."
As supporters and advocates of remixing, we feel that
although highly generative works that lead to highly original
derivatives may be rare and difficult for system designers to
support, understanding remixing dynamics and encouraging these rare
projects remain a worthwhile and important goal." Image: Shutterstock