On interpreting (big) quantitative social science data:
“Just because you see traces of data doesn’t mean you always know the intention or cultural logic behind them. And just because you have a big N doesn’t mean that it’s representative or generalizable.”
“Many computational scientists believe that because they have large N data that they know more about people’s practices than any other social scientist. Time and time again, I see computational scientists mistake behavioral traces for cultural logic.”
“Big Data is going to be extremely important but we can never lose track of the context in which this data is produced and the cultural logic behind its production.”
On interdisciplinarity and methods:
“Each methodology has its strength and weaknesses. Each approach to data has its strengths and weaknesses. Each theoretical apparatus has its place in scholarship. And one of the biggest challenges in doing “interdisciplinary” work is being about to account for these differences, to know what approach works best for what question, to know what theories speak to what data and can be used in which ways.”
Which is why working in interdisciplinary teams where people really listen to each other is so important. Which is why learning beyond gradschool is so important.
On funding agencies and interdisciplinarity:
“I actually think that the funding agencies are going to play a huge role in this, not just in demanding cross-disciplinary collaboration, but in setting the stage for how research will be published.”
This is an important point — and one where I wonder whether the situation over here in Germany isn’t more difficult than in the U.S. Funding agencies over here are incredibily reluctant to make demands to researchers. This has both upsides and downsides, a downside being that there are fewer incentives to cooperate.
On social scienctists and computational scientists joining forces to approach Big Data:
“[..]every discipline has its arrogance and far too many scholars think that they know everything. We desperately need a little humility here.”
Amen. And, interestingly enough, I sense a connection between danah’s argument and Frank Schirrmacher’s views:
Die Informatiker müssen aus den Nischen in die Mitte der Gesellschaft geholt werden. Sie müssen die Scripts erklären, nach denen wir handeln und bewertet werden. Was ist voraussagende Suche und was kann sie? Was ist „profiling“? Wer liest uns, während wir lesen? Technologien sind neutral, es kommt darauf an, wie wir sie benutzen. Um das zu können, brauchen wir Dolmetscher aus der technologischen Intelligenz.
Interestingly enough, danah is the one who’s more critical. Schirrmacher (who isn’t talking about Big Data, but about digital technology in general and about it’s impact on society) demands that computational scientists explain their code to the public — what ranking algorithms do and how context-sensitive ads work. danah criticizes drawing conclusions from automated computational analysis without taking other methods into account. If we start out with simplistic assumptions (e.g. “the people we spend the most time with are the ones closest to us”) we are prone to drawing entirely wrong conclusions, even if our data is beautifully modeled.
I could go on and on here why danah is spot-on here, but instead I’ll just point to the piece itself again.