This past week we met with Dr. Melanie Hughes to consider the practical issues of data production, management, and analysis within the social sciences. One thing that struck me was that, despite our ability to identify issues in the categorization systems presented in class, many of us were unable to express solutions to mitigate these gaps in the data. For example, while discussing the readings, one of my main concerns was the binary presentation of gender when approaching gender statistics. This criticism ignored the both practical issues of increasing the number of variables under study (and weakening the power of the statistical measures used) as well as increasing the visibility of minority groups and exposing them to unnecessary violence (Bailey and Gossett, 2018). Here is where I struggle most, as social scientists we use the available data to not only inform us of the present but also reconstruct our ideas of the past. The lack of data on groups that exist outside the constructed norm (or our knowledge at the time) allow people to act as if these groups are new fads or phases that never existed in the past. The anti-vax movement is predicated on this lack of data, with many suggesting that conditions like autism didn’t exist prior to modern vaccines. Of course, this lack of data on autism in the past is due to advances in knowledge, new diagnostic criteria, and new ways of recording and sharing data rather than the condition itself not existing. More specific categories and more data probably won’t solve this issue, so how do we carefully contextualize the data we collect while maintaining the ability to compare it widely with other vastly different contexts?

I unfortunately missed class last week due to a cold, so I can only draw on information presented in the readings. Across all of the readings, I found the idea of equality as a performative measure to be most intriguing. In a political landscape in which equality is becoming more and more emphasized, it seems that there has been a trend towards focusing on the broad category of “women,” as opposed to focusing on the inherent stratified nature of equality within the gender (i.e. if more women are advancing into positions of political power, yet aren’t using that political power to advocate for the interests of other women, especially those possessing less privilege, is it really a step forward for gender equality?). I was unfamiliar with the statistical complexities of measuring gender inequality (particularly the motivations behind using one measure over another); however, coming from a data-intensive background, I was not surprised that these statistical measures can be heavily affected by data that misrepresents the actual composition of a region. It seems that if people were viewed less as mathematical objects and more as humans, the data might not ignore large sectors of the population in favor of presenting a region as more equal than others.

Although I had suspected that organizations purporting to advocate for equality only really advocate for the advancement of a select few, it was interesting to look at the economic, social, and political factors that influence not just the discussion around gender inequality, but also the policies that are put in place to increase measured equality. Overall, it might be better if organizations understood more of the complexities that the discussion surrounding gender entails and lessened their use of equality as a buzzword that makes them only appear as allies to women, while further acting as perpetrators of inequality through their data and policy curation practices.

Last week facilitated an illuminating conversation due to my previous ignorance of the topic. In many ways, the discussion points were both familiar and unfamiliar. The articles critiqued the shortcomings of data being used to reveal gender equality rates. This was familiar for me; as a historian, I am accustomed to questioning evidence and deconstructing an argument that utilizes controversial data. In the case of our class discussion, it was fascinating to see the ways in which data can be manipulated to make a country appear more equal than it is. I also loved looking at possible solutions to rectify the problems encountered by sociologists, only to realize that many of the solutions suggested in class (including my own) do not adequately address what determines when a country’s gender gap is legitimate. Interpreting data and trying to find a universal model that can determine gender inequality – to the standards of the UN – seems like a gargantuan task.

The articles were unfamiliar not just because the topic looked at contemporary events rather than historical patterns over time, but also because the analysis heavily focused on data points and their inconsistencies. Although the style of writing (Abstract – Conclusion) was much different compared to the narrative story-telling focus of many histories, I really appreciated the articles as an example of the difference between a social science project and a humanities project. This certainly asks the question; how can we intersect the two? Certainly, they do not need to be mutually exclusive. But by intertwining the two, the next question becomes who are you writing this for? And would interpret it still be analyzed as a serious body of scholarship (compared to other social science works)?

 

Bryan Paradis

The most unfamiliar aspects of last week’s readings were the style, structure, and language in the articles. I am far more familiar with narrative writing and using local examples to illustrate characteristics of macro discussions and theories. This genre of writing was overtly structured, formulaic, and distant, and therefore was more difficult for me to follow the arguments. Grouped together as they were, the three articles provide examples of how, over time (2006, 2012, 2017), critiques of the formulas and systems expanded as other social, cultural, and political qualities were taken into consideration.

Because quantitative sociology is not my expertise, I gave great flexibility to the terminology with which I was unfamiliar. As example, words like “decision-making,” “development,” and conflating ideas of gender/sex, seemed to go undefined and/or unchallenged in the pieces we read. My assumption is that these ideas and concepts have been or are continuing to be contested in other arenas of quantitative sociological discourse. While reading, I tried to consider the audience for whom these pieces were written – policy makers at the UN, fellow practitioners, or students – and what the contributions and interventions being made were – conceptual, theoretical, or methodological?

As I mentioned in class, I had most difficulty reading these because, although they discuss societies and how to measure them, there were very few people in the articles’ discussions. I felt most comfortable reading Weber’s piece, Politics of ‘Leaving No One Behind,’ because readers saw a glimpse of what the neo-liberal Sustainable Development Goals and other polices look like on the ground. Providing the example of the Bolivian ‘water wars’ and the “more than 70,000 people who took to the streets in protests [of the policy to privatize water],” Weber illustrates the local implementation of such broad policies.[1] What other examples could highlight the impact of programs like the United Nations Development Programme, Millennium Development Goals, and Sustainable Development Goals?

Large-scale quantitative studies like those discussed in the articles could aid other social scientists and researchers in the humanities when identifying points of interests for further study. One my ask, for example, why a certain country has a higher or lower gender empowerment and equality ratio and develop projects to address the inconsistencies.

[1] Weber, 403

In our readings and conversations over the past week, I was most familiar with the notion that ideological biases inform the way that information is presented. As I mentioned at the close of my blog post for last week, I have been thinking about the presentation of information, informed by individual, social, and cultural biases, as knowledge in scholarly narratives of the history of art. As a brief (counter) example, in his essay “Race, Nationality and Art” (1936), the art historian Meyer Schapiro argues against the art historical notion that national and racial groups have fixed psychological qualities that are evident stylistically in works of art produced by members of these groups.

I was most unfamiliar with the concept of statistical indicators as discussed in our readings and in our class meeting with Melanie Hughes, and I was glad that we were able to talk with Hughes about her ongoing SDG indicator work.

I was most intrigued by the discussion of economic growth in Fulvia Mecatti, Franca Crippa, and Patrizia Farina’s article “A Special Gen(d)re of Statistics: Roots, Development and Methodological Prospects of Gender Statistics.” In their discussion, the authors include a quote by Saadia Zahidi from the World Economic Forum in 2010 that frames the education, empowerment, and integration of women and girls in terms of a necessity in order for economic recovery and growth (457–458). In reading this section of their article, I became concerned with the motivations that may be behind gender equity that prioritize economic growth. In their conclusion, Mecatti, Crippa, and Farina argue that eliminating gender-based inequality is in the interest of society as a whole because society would benefit from the resulting social development and economic growth. While the authors’ statements here may be read as, and may have been intended as, a persuasive argument meant to raise interest in eliminating gender-based inequality, I still question instances in which social justice is seemingly made palatable to those not directly affected by the issue in question—that is, I feel that the fact that education, empowerment, and integration are and would be beneficial for women and girls should be reason enough for others to support initiatives that would bring those goals within reach.

As I mentioned in class, I just finished Perez’s Invisible Women: Data Bias in a World Designed for Men (2019). Perez cites some of the kinds of research we read for last week in her book.  She talks about how problematic many research methods are regarding everything from city planning to medical research. So many of these areas do not account for gender difference, and therefore they privilege male bodies and needs  as “typical” and women as “atypical.” In some cases the gender data gap leads to daily inconvenience for women, in other cases, it kills them.

The need to consider culture as part of this was also discussed. She tells of companies that made new “clean” stoves to help women in the developing world to avoid smoke inhalation caused by cooking over open fires. However, they failed to take into account the actual needs of these women in their design. They designed stoves that took longer to cook food, so many women reverted back to open fire cooking. In a report they then blamed lack of training of the women and not the stove design (and flawed data-gathering) as the problem that needed to be addressed so the women would start using their products.

This context, and the discussion last week, got me thinking about how data gathering often tries to sterilize the messiness of the world. Women can be hard to gather medical data on, for instance, because of hormonal fluctuations throughout the month. But not dealing with the messiness (like the academics in our case study were shown to always bring up) will continually leave someone out. While not all data can be collected perfectly, striving for better gender representation is surely a worthwhile goal. Women are half of the population, after all. In other words–let’s keep being academics and complicating things!

(Note: sorry I’m missing page number citations here. I listened to the audiobook and it has since been returned to the library!)