Giorgos Cheliotis of the National Univ of Singapore, and a visitor researcher here at the Center, is giving a lunchtime talk at the Berkman Center called “Mapping the Global Commons: A quantitative perspective on free cultural practice.” How large and free are the Commons? (He’s excluding open source software from his discussion of the Commons)

Giorgos has been working with Creative Commons. He points to a number of works, including by Lessig, and David Bollier’s “Viral Spiral,” which is a history of the digital commons. If the movement is old enough that histories are being written, Giorgos says, it may be time to take a fresh look at it.

He says the digital commons consists of shared resources, users, open licenses, and remixes. To measure its size, you can ask how people use it, how many resources in it, how quickly it’s growing, and how much is contributed back to the pool. How free the pool is will obviously affect how its gets used and remixed. All this is hard to measure, Giorgos says, because there’s no central registry. One approach would try to count everything that’s there. Another uses estimates, community-specific data, and external reports and local knowledge. Giorgos uses the latter technique. There is a trade-off between scale and accurate/richness of the data set.

He and his colleagues are building a live-data wiki platform to track the global development of open licensing (CC only for now): http://monitor.creativecommons.org. (It’s early beta, pre-release, and still under development.) Giorgos walks us through it. [You can give it a try yourself. It’s self-explanatory.] AT the moment, the wiki says that there are 170,268,161 Creative Commons-licensed works. At the site you can break this down by region. Asia is growing quickly. Brazil has lots. Spain is ranked #1. (You can zoom in on the map by drag-selecting an area.)

The project is aimed at the media, researchers, funding organizations…

The regions each have a “freedom score” that weights the CC licenses by how restrictive or permission they are. The overall weighted average is 3.29 out of 6. US: 3.1. Spain: 3.47. Brzil: 2.34. Thailand: 2.58 (which is a decrease). Korea: 1.76 (but lots of licenses). Giorgos says that presenting this data sometimes nudges people to work on boosting their country’s score.

The tables of data and the maps generated from them are automatically generated and cannot be changed by wiki users; the annotation and commentary can be changed. To see an example of a manually-curated page, see Singapore‘s. Giorgos points out that this raises synchronization issues: The data is updated but the narrative may not be.

How now asks how much is being remixed. They’ve focused on ccMixter, where everything has a CC license and can be remixed. You can see the chains of influences. He shows a visualization of the data: Each track is a node, with lines connecting them to remixes. The maximum path length is 6 (a remix of a remix of a remix, etc.) But it drops off quite steeply after path length 2. 60% of uploaded items don’t get remixed, but remixing accounts for more than half of the total production volume. In a“bow-tie” analysis, there’s a core of about 12% core contributors whose authors’ tracks are linked to and who link out; if you take contests out of the picture, the core goes up to 18% (although about the same absolute number) and the “tendrils” go down from 50% to 20%. [Giorgos presents some other visual analyses, but I can’t follow the visual presentation of quantitative information. Sorry. It’s a brain problem of mine.] In the core, there are more reciprocal relationships, which seems to show that the members of the core community see one another as peers.

33% of generation 1 remixes are contest entries: An artist or label sponsors a contest for the best remixes of a track. Contests attract one-time remixes who are “not productive otherwise in the community.” But, are contests part of a sharing economy, he asks? Some scholars say that contests help strengthen a sense of community. Giorgos is uncertain about what to make of contests.

Q: [me] Public domain? Media types?
A: Neither of those types of metadata are easily available.

Q: CC has the metadata about the media type. And it would be interesting to see how the licenses vary by media type.
A: It’s possible, but we haven’t done it so far. I have noticed that photographers tend to be more protective of their content than are musicians.

Q: Maybe photographers are worried that their work will be used to create a false image, which isn’t an issue for musicians.
A: I think that’s probably right. Music is usually used for entertainment. Photos are also used for information.

Q: What are you aspirations for this as data collection project?
A: I was motivated initially to do this [Tags: creative_commons copyright copyleft ]

Categories: misc dw