Weeknotes are my way of reflecting on my readings, research and thoughts from the week. They link and contrast experiences and observations that happen to have occurred in a short period of time. I am writing these notes quickly, doing my best to correctly represent the sources, but I would love to learn if I am mistaken in my understanding in any way. You can contact me on https://climatejustice.social/@PenguinJunk.
Its been a while - a whole month - which makes this more like “monthnotes”. Here’s a summary of what I’ve been up to…
tl;dr
Mostly I’m collecting and synthesising data from interviews with scientists who have engaged to a greater or lesser extent with public policymaking. Currently I’m looking at a rich and diverse thicket of data that I am gently pruning into something more a little more structured.
so what is my thesis about?
After some cracking, and extremely pertinent feedback from my supervisor, I spent 5 days writing the introduction to my thesis. This felt rather heretical at first as I’ve always written the introduction last. But it forced me to get my thoughts in order and lay them out for someone else to read. I’m rather too shy still to put them here, because I know they’ll change. However, in summary, my research is looking at the roles that scientists play at the interface with policy by considering aspects of their research, the experiences that they have, the contexts that they engage within, and the strategies that they use. I currently intend this to be rather descriptive, since there is a great deal of normative literature on the topic already. My supervisor seemed happy and I feel much more confident about what I am attempting to achieve. Additionally, due to confusion about which documents were the most recent, I have now set up a thesis document in LaTeX and am constantly dropping thoughts and summaries into it. I hope they will make the final write-up easier - but they may make it more overwhelming!
and what are the data?
In the last month I have also arranged the interviews of around 14 scientists and am over halfway through these. They have been intense, interesting, sometimes thoughtful and other times surprising. Its been such a privilege and a learning opportunity well beyond the purpose of writing a masters thesis. I am a little way into sythesising these interviews, having had some false starts. I first tried to use a wholly behavioural framework to analysis the transcripts but this was unsatisfatory - it didn’t allow me to find labels for a good proportion of the fascinating insights I had been gifted. I found an alternative framework, from a similar study in health policy, which I used as a starter and have iterated over, expanded and contracted, and now have somewhat my own structure. I’m sure it will change quite a bit more so I’ll wait until the final report to describe this framework and, perhaps, some of the process by which I arrived at it.
Tools for the job
I also had some software pain. Having no previous qualitative data analysis (QDA) experience, I used the one QDA software that was available through university. However, over a couple of weeks I had more and more problems. I had a small data corruption and I found that the labels I thought I’d applied had not been applied, or were applied to whole paragraphs of text. This is a huge risk when I have a hard deadline. After asking around and trying an open-source version, I gained a little confidence to go my own route and have worked out how to use a combination of CSVs in Excel (yeah, I know) and regular versioning using git and I feel I have a more usable, more robust process. Moreover, I found that when I reformated my transcripts for CSV (basically 1 statement per line), I noted much more nuance than previously and found that I’d sometimes misunderstood what participants had said.
Comments powered by Disqus.