Lippmannian device: “Making an Issue Cloud”
An introductory tool lecture on the Lippmannian device by Prof. Dr. Richard Rogers.
An organization’s issue agenda (or commitment), for example Public Knowledge, a digital rights NGO, has issues. Which are they most commited to? Related to Bruno Latour. What Latour wanted was a tool that Lippmann wanted: A simple machine that could detect the partisanship of an actor, bias. The Lippmannian device is named after Walter Lippmann, and provides a coarse means of showing actor partisanship. Rogers sees issues articulations as commitments.
Public Knowledge has a very specific issue language which can be found on their website. it is a flat issuespace, every issue seems to have the same weight. What is the distribution of issue commitment in this space?
Multiple sources, multiple issues
What is the agenda of the global human rights network? Which issues are at the top and at the bottom of the agenda? What is the current level of commitment to a particular issue?
Method:
- Take three good lists of human rights organizations (global south/Choike, global north/Amnesty, UN’s/UDHR)
- Triangulate lists, which organizations appear on at least two lists
- Make a list of all issues listed on all Websites
- Put all issues in bottom box, all urls in the top box, and hit go (but realize you are querying a lot of sites for a lot of issues, thus tick 1 result. What you are doing, translated into a manual query: site: amnesty.org “Children in War” But for each issue and each site.
What is at the bottom? What are the non-issues. What do researchers think is important may not be important at all because nobody actually mentions that issue. What are issues, what are words, categories, taxonomies? How to identify bogus results?
Partisanship in these specific clouds (on global human rights issues) is translated into a distributed matter of concerns. Another output is the source cloud, as can be found on the example project website.
