We did it. We found the Holy Grail.
For us over the at GMM, the grail in question is quantitative data related to intakes at Grosse Île during the terrible Famine year of 1847. We’ve been searching across the digital and print archive-scape to find the elusive “Weekly Return of Sick in the Quarantine Hospital,” and finally, one grey Montreal afternoon, the words miraculously appeared on my screen during a detailed search on Canadiana Online. I should be more specific; I found entries for May 1847. Still, this find helped to fill a gap that Jane, Giselle, Sadie, and I had been half trying to ignore out of frustration from not finding the associated records, and half manifesting.
After a text message exchange that featured many lines entirely in upper case, a lot of exclamation marks, and no small number of emojis, I got to work processing the data, taking note of the number of admissions, the number of sick who had sadly succumbed to illness, the ages of the sick, the number of discharges, in addition to the ships on which they sailed over and the duration of their hospitalisations.
In addition to being somewhat shaken by language with which I am just a little bit too familiar in the age of Omicron, the exercise of finding and then processing quantitative data that represents suffering in numerical form also made me pause to consider what I conceptualize as the number paradox. We’ve been in an extended pre-regression analysis phase at the GMM, so, to be sure, these findings would be helpful to our project and would serve as the basis for infographics. Visual representations of collected data not only allow us to ultimately communicate our findings in a compelling way, but also represent our data back to us, and encourage us to think about the data from a different perspective. However, the necessity of considering numerical findings in concert with qualitative data was very much illuminated for me during this experience.
In the weekly return of sick for May 23-29, 1847, Dr. Douglas relays that 71 people died. All succumbed to “fever.” I started thinking about what I could “do” with these numbers. In calculating the average age, there is not much discrepancy in terms of the mean and median (23.58 and 23 respectively) with the mode (30) somewhat higher than these figures, with an overall range of 59.97 years. There are no outliers in the set. In this dataset, one was as likely to be three as to be 43 in facing death from fever. What does this tell us about the experience of being either of those ages at the time of death? What does this tell us about the personhood of the sick, those left behind, those who might have sadly died before? How did I feel about looking a list of names and calling it a “dataset”? The set can’t tell us much about the emotions of mothers and fathers (I think of this because of one of the GMM’s–fingers crossed–future iterations, which focuses on motherhood; see paragraph beginning with “For one of this autumn’s proposals…”). This brings to mind recent research that counters claims about early modern parents feeling grief over the loss of their children “less” than their modern counterparts, given the higher rate of infant mortality (see this post, for example, that references French medievalist Philippe Ariès’s claim). That a child was just as statistically likely to succumb to illness as an adult can tell us about the nature of the virus and the material realities of emigration, but I would argue that it reveals (on its own) little more about dying than does the fact that two thirds of those who died that week were men tell us about gendered experiences of sickness, health care, dying, and emigration.
Look, I know what I’m talking about here is basic descriptive statistics and not sophisticated quantitative analysis. However, what is left after asking myself these questions is a sense of pride in the team’s efforts, and gratitude for the leadership of Dr. McGaughey, in prizing our qualitative findings – however isolated, seemingly obscure, or signalling the beginning of a detour that could chew up several contract hours. In my mind, this allows for the quantitative to be the springboard for more investigations, part of a dialogue, another way to create context, rather than the crux of the project or the ultimate goal. After chasing the numbers for so long, and in finally getting them, I find myself with more questions than answers. Challenge accepted.