We propose to explore development of two methods for monitoring community well-being, both relying heavily on electronic communications. The first will involve attention-slice self-reporting as a complement to survey data e.g. available through Gallup/Healthways 5 reports [13]. A video that a former undergraduate Ellie Ordway put together in 2010 for a Scientific American contest may be found linked on the web at http://newton.umsl.edu//philf/slice.html. More recent links to a Google Forms “attention-slice updater” may be found at http://www.umsl.edu/~fraundorfp/ifzx/taskLayerMultiplicity.html. We’ve since learned a good deal about web applications in context of our electron detectives and real time sheet music google sites, and our task would be to put something together that brings a stream of data whose reliability we could then begin to examine.
The second method was suggested to me by Physics graduate student Zak Jost. It involves the categorization of public datastream data e.g. from https://dev.twitter.com/streaming/overview . Like our attention-slice self-reporting and unlike the survey data, this datastream could have real-time significance. The fact that it does not involve explicit self-reporting makes it complementary to them, however, both in terms of its potential strengths and weaknesses. Again, we propose to find a way to tap that datastream to obtain information on community task layer-multiplicity, perhaps with help from Amazon’s Mechanical Turk. If human pattern-recognition by Mechanical Turk (https://www.mturk.com/mturk/welcome) or some other such resource is needed, this will be reflected in the final proposal budget, although we are not assuming that will be the case at present.
The hypotheses we’d like to explore with these models are as follows: (A) Do our real-time self-reporting and our communication datastream data on task-layer multiplicity correlate in time and by region with one another? (B) Do our measures correlate with the task-layer multiplicity data available from Gallup/Healthways 5 (and any other multi-layer survey instruments we can identify)? Finally, (C) do these task-layer multiplicity data correlate with measures of community resource consumption (like google’s free-energy per capita data at http://www.google.com/publicdata) and community well-being? We will work to make our proposal for work to these ends more explicit, as well as to identify cross-disciplinary collaborators, in the time between this letter of intent and the full proposal deadline.