I started week 3 in a state of muted panic, mostly related to the monster of a 10-page classification scheme I finalized a draft of last Friday. On Monday I realized that while this is a really good entry point to the data in its current state it’s completely unusable to classify anything. 5 pages is probably the maximum that any “usable” schema can be. I also realized that I needed to get my head up and away from the mess if I was going to return later with any newfound clarity.
So I refocused my attention on three main areas: understanding how different data policies hold up in court, understanding what different academic circles are really talking about when they talk about “data policy,” and starting to codify my increasing knowledge in a usable way. On the first point, I learned that the academic study of law produces really good papers (in retrospect this shouldn’t be surprising given that, for lawyers, eloquence is basically a pre-requisite to success). My favorite paper discussed how the current cognitive models of contract law are drastically outdated. Essentially, most contract law was written when paper was still a really big deal and the simple act of creating a physical document designated a high level of commitment and intent. Since then, the power of paper has been slowly but surely diminishing, beginning with the printing press and moving through the invention of the personal computer up to the ubiquity of Twitter’s 140-character updates. But our understanding of contracts has not essentially changed; the process of signing a waiver is essentially the same now as it was 500 years ago in spite of the differences between virtual consent and the signage of a unique physical document with a quill pen.
In the United States, discussions about data policy are usually associated with different spheres– data policy to deal with financial information, data policy to deal with health care records, data policy that the US Government is required to abide by. One interesting area of study with potential implications for PPSR is data policy involved in conducting research with Indigenous populations. Discussions about the policies required to conduct research with Indigenous populations center around questions of power, privilege, and ownership– three themes that resonate with PPSR community concerns.
As a PhD student beginning to think about dissertation work, I’m not always the most practical worker. Because of this limitation I’m especially pleased to write about my new focus on producing practical deliverables. I spent portions of Thursday and Friday learning PLONE, and the result is that materials on data policy are beginning to populate the Citizen Science Central website (the exemplars that I promised last week are published here). While these pages are in no way finalized, it’s nice to be able to see a concrete product of my work. To that end I’ve also set a (reasonable) goal of writing 20 sentences per working day for the Practitioner Training guide that I’ll need to produce in 3 or 4 weeks time.
Well, for what it’s worth, the first round of my dissertation coding schema ran to 23 pages, but it wasn’t structured in an outline, just flat text. It’s pretty common to start broad and then winnow down to something functional, though your schema deals more with yes/no objective evaluations of content rather than interpretation of text, so the schema wouldn’t evolve the same way as it would in more interpretive grounded theory.
The trick, once you’re satisfied enough (good, not perfect, thanks!) is figuring out what part of the schema you need to apply to accomplish your ends. Just descriptive summaries can be interesting, and from a conceptual standpoint, this schema is the type that would work well for quantitative analysis.