I've never presented at anything like PyData either, but am very much on board with the idea of your talk (would love to hear more about it!). I think a key thing to do in terms of framing the talk will be to have some really clear demonstrations of where and why social/human science requires different things from Python. One thing might be our central commitment to methodology (i.e. the social sciences are typically very reflexive in terms of how we think about methods) - in this regard, just using third-party software and algorithms often isn't good enough for our purposes, we need to be able to see and account for (and possibly also tweak) how these things work in order to do good social science research with them. And this sort of feeds into what we need out of tools like Python - libraries like matplotlib, even though it's a pretty standard tool across a range of disciplines, most will just draw on matplotlib as a graphing library without getting 'under the hood' and taking advantage of it being an open source thing. The fact that it's open source is interesting to us, however, in that that's what gives us the access we need to be reflexive about our methods (as long as we can read the code, that is). Even for something as established as the idea of a graph to visualise data, this is something we probably want to be critical about inasmuch as the algorithms that govern the visualisation thereby also govern our analyses of it! So this seems to me to be an aspect of programming where we have a very different interest in programming than 'hard science' researchers, and it leads us to do different things (which, I guess, we might call "having a more critical engagement with programming tools", in a typical sociological parlance?).

Contrast this with the grid computing project that the National Centre for e-Social Science (at Manchester) was involved in some years back (maybe 2007/2008 if I remember right?) - on a technical level they built a fully operational grid computing system that was, in principle, available for use by any social science researcher. It's the kind of thing that had been around for a good few years in the 'hard sciences' already (I think), and it was assumed that making this technical infrastructure available would lead (computational) social scientists to start asking different questions - "if you build it, they will come" kind of thing. But, the problem was that nobody was really interested in research questions where the kinds of processing power offered by grid computing would be a necessity! It didn't really address any of the problems social scientists were having in terms of getting to grips with different (i.e. digital) datasets, and nothing really came of it. Again, it kind of shows, in a roundabout way (sorry!) that the things that work for the 'hard sciences' don't necessarily easily transpose to the social sciences.

Anyway, apologies for the rambling thoughts John, would be very keen to hear your thoughts on the conference if/when you do end up going, please keep in touch!

From: Programming as Social Science (PaSS) <[log in to unmask]> on behalf of Juan C. Correa <[log in to unmask]>
Sent: 01 March 2017 17:39
To: [log in to unmask]
Subject: Re: programming & interpretive social science

Hi John

Your talk looks interesting. Yet, you have said something that I think is crucial: "The kind of knowledge generated in the human sciences is different, so the ways in which it is produced differs as well."

As a Psychologist I am convinced that Python and other computational tools are calling our attention to re-consider the traditional ways we social scientists follow to collect relevant data...

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Juan C. Correa
https://sites.google.com/site/jcorrean

From: Programming as Social Science (PaSS) <[log in to unmask]> on behalf of John D. Boy <[log in to unmask]>

Sent: Wednesday, March 1, 2017 12:18:50 PM
To: [log in to unmask]
Subject: programming & interpretive social science

I was very happy to find out about this list yesterday. I think Phillip and Jonathan have really identified an area where a lot of individual efforts and researchers are in search of coherence and community. Thanks!

Since joining, I've been toying around with a few things I'd like to send to the list -- there's so much to discuss! For practical reasons (because it's due in a few days), I thought I'd start by sending a draft of an abstract I intend to submit to PyData Amsterdam. I've never presented in that kind of venue, and I am curious whether this list might have any advice on how to frame my talk, which is really just intended to give the PyData audience insight into how some colleagues and I (tech-savvy sociologists who are more at home on the interpretive than on the quantitative side of the discipline) work with the scientific Python ecosystem to do our work.

Also, I will hang around in the IRC channel #pass on the OFTC network for the next couple of days, in case anybody wants to join. Might be a nice additional venue to further these kinds of discussions.

Anyway, below is what I have so far. I'm curious what you think.

Kind regards,
John

A Python Stack for the Human Sciences: Approaches and Applications

A thriving multitude of academics uses Python in their research work. While it is diverse and dynamic, the Scientific Python community is often dominated by physicists, biologists and, to a lesser degree, computational social scientists. The visibility of these disciplines and their applications means that Python is widely recognized as a powerful tool to create positive knowledge in the hard sciences and in data science. But Python has also been adopted by people in the human sciences, including digital humanists and interpretive social scientists. (We count ourselves among the latter group.) The kind of knowledge generated in the human sciences is different, so the ways in which it is produced differs as well. Scholarly habits differ, and so do the demands on the research process (e.g., with regard to reproducibility, the tradeoffs made between reliability and validity, or the role of theory and theory development in our scholarly pursuits). Scholars in the human sciences wield Python as a tool in ways that are unique and not often seen by others in the community. This talk provides insight into some approaches and applications of interpretive social scientists who use Python to study social, spatial and cultural patterns to support critical inquiry into everyday life.

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