i’ve started working on my dissertation // diversity in open source // reflexive data science

by Sebastian Benthall

I’m studying software development and not social media for my dissertation.

That’s a bit of a false dichotomy. Much software development happens through social media.

Which is really the point–that software development is a computer mediated social process.

What’s neat is that it’s a computer mediated social process that, at its best, creates the conditions for it to continue as a social process. c.f. Kelty’s “recursive public”

What’s also neat is that this is a significant kind of labor that is not easy to think about given the tools of neoclassical economics or anything else really.

In particular I’m focusing on the development of scientific software, i.e. software that’s made and used to improve our scientific understanding of the natural world and each other.

The data I’m looking at is communications data between developers and their users. I’m including the code, under version control, as this. In addition to being communication between developers, you might think of source code as a communication between developers and machines. The process of writing code as a collaboration or conversation between people and machines.

There is a lot of this data so I get to use computational techniques to examine it. “Data science,” if you like.

But it’s also legible, readable data with readily accessible human narrative behind it. As I debug my code, I am reading the messages sent ten years ago on a mailing list. Characters begin to emerge serendipitously because their email signatures break my archive parser. I find myself Googling them. “Who is that person?”

One email I found while debugging stood out because it was written, evidently, by a woman. Given the current press on diversity in tech, I thought it was an interesting example from 2001:

From sag at hydrosphere.com Thu Nov 29 15:21:04 2001
From: sag at hydrosphere.com (Sue Giller)
Date: Thu Nov 29 15:21:04 2001
Subject: [Numpy-discussion] Re: Using Reduce with Multi-dimensional Masked array
In-Reply-To: <000201c17917$ac5efec0$3d01a8c0@plstn1.sfba.home.com>
References: <20011129174809062.AAA210@mail.climatedata.com@SUEW2000>
Message-ID: <20011129232011546.AAA269@mail.climatedata.com@SUEW2000>


Well, you’re right. I did misunderstand your reply, as well as what
the various functions were supposed to do. I was mis-using the
sum, minimum, maximum as tho they were MA..reduce, and
my test case didn’t point out the difference. I should always have
been doing the .reduce version.

I apologize for this!

I found a section on page 45 of the Numerical Python text (PDF
form, July 13, 2001) that defines sum as
‘The sum function is a synonym for the reduce method of the add
ufunc. It returns the sum of all the elements in the sequence given
along the specified axis (first axis by default).’

This is where I would expect to see a caveat about it not retaining
any mask-edness.

I was misussing the MA.minimum and MA.maximum as tho they
were .reduce version. My bad.

The MA.average does produce a masked array, but it has changed
the ‘missing value’ to fill_value=[ 1.00000002e+020,]). I do find this
a bit odd, since the other reductions didn’t change the fill value.

Anyway, I can now get the stats I want in a format I want, and I
understand better the various functions for array/masked array.

Thanks for the comments/input.


I am trying to approach this project as a quantitative scientist. But the process of developing the software for analysis is putting me in conversation not just with the laptop I run the software on, but also the data. The data is a quantified representation–I count the number of lines, even the number of characters in a line as I construct the regular expression needed to parse the headers properly–but it represents a conversation in the past. As I write the software, I consult documentation written through a process not unlike the one I am examining, as well as Stack Overflow posts written by others who have tried to perform similar tasks. And now I am writing a blog post about this work. I will tweet a link of this out to my followers; I know some people from the Scientific Python community that I am studying follow me on Twitter. Will one of them catch wind of this post? What will they think of it?