I recently had the privilege of being an invited speaker at the Gendered Creative Teams workshop hosted by Central European University and organized by Ancsa Hannák, Roberta Sinatra, and Balázs Vedres.
It was a truly remarkable gathering of scholars, researchers, and activists, featuring two full days of provocations and rich discussion.
Perhaps one of the most interesting aspects of the conference was that most of the attendees did not come from a scholarly background focusing on gender, but rather came at the topic originally through the dimension of creative teams. The conference, then, provided an opportunity to think more deeply about this latent – but deeply salient – dimension of the work.
Because of this, one of the ongoing themes of the conference – and one which particularly stuck with me – focused on the subtle ways in which the patriarchy shapes the creation and distribution of knowledge.
As some of you may know, I am fond of quoting Bent Flyvbjerg’s axiom: power is knowledge.
As he elaborates:
…Power defines physical, economic, ecological, and social reality itself. Power is more concerned with defining a specific reality than understanding what reality is. …Power, quite simply, produces that knowledge and that rationality which is conductive to the reality it wants. Conversely, power suppresses that knowledge and rationality for which it has no use.
This presents a troubling challenge to the enlightenment ideal of rationality. As scientists and researchers, we have a duty and a commitment to rationality; a deep desire to do our best to discover the Truth. But as a human beings, living in and shaped by our societies, we may simultaneously be blind to the assumptions and biases which define our very conception of reality.
If you’re skeptical of that view, consider how the definition of “race” has changed in the U.S. Census over time. The ability to choose your own race – as opposed to having it selected for you by interpretation of a census interviewer – was only introduced in 1960. Multiracial recordings only became allowed in 2000.
These changes reflect shifting social understandings of what race is and who gets to define it.
We see a similarly problematic trend around the social construction of gender. Who gets to define a person’s gender? How many genders are there? These are non-trivial questions, and as researchers we have a responsibility to push beyond our own socialized sense of the answers.
Indeed, quantitative analysis may prove to be particularly problematic – there’s just something so reassuring, so confidence-inducing, about numbers and statistics.
As Johanna Drucker warns of statistical visualizations:
…Graphical tools are a kind of intellectual Trojan horse, a vehicle through which assumptions about what constitutes information swarm with potent force. These assumptions are cloaked in a rhetoric taken wholesale from the techniques of the empirical sciences that conceals their epistemological biases under a guise of familiarity. So naturalized are the Google maps and bar charts of generated from spread sheets that they pass as unquestioned representations of “what it.”
As a quantitive researcher myself – and one who is quite fond of visualizations – I don’t take this as a admonition to shun quantitive analysis all together. But rather, I take it a valuable, humanistic complication of what may otherwise go unobserved or unsaid.
Drucker’s warning ought to resonate with all researchers: our scholarship would be poor indeed if everything we presented was taken as wholesale truth by our peers. Research needs questioning, pushback, and a close evaluation of assumptions and limitations.
We know that our studies – no matter how good, how rigorous – will always be a simplification of the Truth. No one can possibly capture all of reality in a single snapshot study. Our goal then, as researchers, must be to try and be honest with ourselves and critical of our assumptions.
As Amanda Menking commented during the conference – it’s okay if you need to simplify gender down from something that’s experienced uniquely for everyone and provide narrow man/woman/other:___ options on a survey. There are often good reasons to make that choice.
But you can’t ignore that fact that it is a choice.
If you choose to look at a gender binary, ask yourself why you made that choice and explain in at least a sentence or two why you did.
Similarly, there are often good reasons to use previously validated survey measures: such approaches can provide meaningful comparison to earlier work and are likely to be more robust than quickly making up your own questions on the day you’re trying to get your survey live.
But, again, such decisions are a choice.
If you use such measures you should know who created them, what context defined them, and you should critically consider the implicit biases which may be buried in them.
All methodological choices have an impact on research – that’s why we constantly need replication and why we all carry a healthy list of future work. Of course we still need to make these choices – to do otherwise would paralyze us away from doing any research at all – but we have to acknowledge that they are choices.
Ignoring these complication may be an easier path, especially when it comes to aspects which are so well socialized into the broader population. But that easier path reduces scholarship to the level of pop-science. A quick, flashy headline that glosses over the real complications and limitations inherent in any single study.
You don’t have to solve all the complications, but you do have to acknowledge them. To do otherwise is just bad science.
