This week’s assignment was about debunking myths: employing methods to shine truths into the minds of readers who are often systematically, politically, stubbornly biased against the facts given.
Often it is mind-boggling that these myths are able to persist: issues like human-caused climate change are so heavily supported by scientific evidence and harbor such great consequences, it seems that common sense calls for a belief in its existence. Mythbunking, however, is rarely about common sense, and almost always about human psychology. It’s a surgical operation: “when you debunk a myth, you create a gap in the person’s mind. To be effective, your debunking must fill that gap” [the Skeptical Science Debunking Handbook].
A myth, and thus mythbunking, therefore, seems to be defined not by what class of problem the issue is in (environment, vaccination, gun control), but rather one’s attitude towards the concept at hand. When there is a lag between perception and reality, and an avoidance to voice the reality, despite the support of statistics, then we have a myth of the Skeptical Science kind. Even though the issue that I ended up exploring in the end isn’t as clearly divided as black and white (or maybe it is precisely too much black and white), it follows all of these trademark characteristics.
Although it would be hard to believe that anyone who has spent time at MIT to refuse to acknowledge any skew in demographics, the narrative that emerged out of my attempts to retrieve facts about the degree in which diversity was a problem in recruiting and admissions reinforced myths more subtle and potentially harmful– those of our own misguided perceptions.
Gender Representation is significant issue in both student and faculty body.
In terms of sheer numbers, there are far fewer women than men throughout the lab. In a total of 198 students listed on the Media Lab webpage, only about 25% (or 50 students) were female. 7 out of 27 groups, or about 26% (my own included) have only 1 female student.
Tokenization (not as in lexical analysis, but as in there is a token representation of a minority) is harmful in that it encourages stereotype threat: the risk of confirming to stereotype, leading to underperformance of the individual in a workplace or academic environment (for a summary, see this fact sheet).
Faculty fares even worse in terms of gender, with only 5 female professors out of 31 total, or 16%. Of those 5 female professors, only 2 are tenured faculty, or 6%. This is half the national average for Engineering, last measured in 2011 by the NSF.
Moreover, in my attempts to obtain these statistics, I found it difficult to get a unified response from directors and administrators. In the end, I was advocated against using the data that I had previously found from the official Diversity Committee, whose website does not contain any concrete figures.
As a result, I used language processing on the listings on the Media Lab website and inferred the most probable gender of students and faculty by name, using the Gendre API which searches databases of first and last name combinations by country. It is important to note that my counts are of inferred gender by probability, and an estimate of how a student might actually self-identify.
Although the female students and faculty who are within the lab have a strong and significant presence (such as Pattie Maes, who is the Associate Head of the MAS Program and whose brief report earlier this month prompted me to explore this issue in the first place), this lends an exaggerated effect of parity, which isn’t the case.
The Media Lab has an even bigger problem with minority representation. Whereas gender inequality has made some small but not insignificant strides in the last two decades, the number of URMs (under represented minorities) has stayed extremely low and in some cases worsened. I was asked not to cite these materials in my work. I was not able to use linguistic analysis to try to infer people’s race and ethnicity, for both practical and ethical reasons.
