April 3, 2006
Last weekend’s Cultural Studies conference reminded me of a viscous cycle that many humanities-oriented researchers are being subjected to. Disciplines such as educational research, ethnography, anthropology, cultural studies, sociology etc have effectively been colonized by the methodology of the social sciences and they are being forced to play a numbers game which they may not be suited for.
Many projects striving for credibility are subjected to the tyranny of statistics – forced to transform their qualitative information (interviews, transcripts, first person accounts) into quantitative information through the process of coding. This reduction forces the data into buckets and creates a significant degree of signal loss, all in the name of a few percentages and pie-charts.
Perhaps we have lost sight of the motivation for this reduction – the substantiation of a recognizable, narrative account of a phenomena, supporting an argument. Arguably, the purpose of the number crunching is to provide supporting evidence for a demonstrable narrative. Modern visualization techniques may be able to provide one without all the hassle.
True, this is not always the only reason that qualitative is transformed into quantitative data, but advanced visualization techniques may provide a hybrid form that is more palatable to many of the researchers active in this area, and is still a credible methodology. It seems as if many people are being forced into coding and quantification, when they aren’t thrilled to be doing so. But the signal loss that coding is responsible for, all in the name of measuring, might be unnecessary if people think about using data visualization tools, that comprehensibly present the data, in all of its richness and complexity, as opposed to boiling it down to chi-squared confidence levels (and does this false precision actually make any difference? Does a result of 0.44 vs. 0.53 tell significantly different stories?)
In a thought provoking post on the future of science, Kelly enumerates many of the ways new computing paradigms and interactive forms of communications might transform science. The device that I am proposing here might lead to some of the outcomes Kelly proposes.
For a better idea of the kinds of visualization tools I am imagining, consider some of the visualization work on large email corpora coming out of the M.I.T. media lab, or the history flow tool for analyzing wiki collaborations, but even the humble tag cloud could be adapted for these purposes, as the power of words and visualizing the state of the union demonstrate.
Crucially, tools analogous to Plone’s haystack Product (built on top of the free libots auto-classification/summarizer library) might help do for social science research what auto-sequencing techniques have done for biology (when I was a kid, gene sequences needed to be painstakingly discovered “manually”).
The law firms that need to process thousands of documents in discovery and the commercial vendors developing the next generation of email clients are already hip to this problem – when will the sciences catch up?
For any of this to happen the current academic structure needs to be challenged. The power of journals is already under attack, but professors who already have tenure can take the lead here and pave the road for their students to follow.