Students in Professor Havens’s World War II class, here’s a link to your syllabus.
Spring 2018 East Asian Studies students, here is a link to your syllabus, with hyperlinks to the PDFs.
Faithful readers of this blog (all one of you) will notice that I haven’t posted in almost a year. It’s not that I’ve had nothing interesting to say, but rather that I’ve been too busy with those interesting things to write about them for the blog. Here’s a brief rundown.
In the summer of 2014, my family moved to Fairfax, VA, when my husband was hired by George Mason University. For the 2014-2015 school year, I commuted to Boston from Virginia almost every week so I could finish my coursework at Northeastern University. In August 2015, I passed my comprehensive exams and defended my dissertation proposal, officially becoming a PhD candidate. For the past year, I’ve been researching and writing my dissertation, as well as continuing to work on the Viral Texts project.
The Viral Texts project has been part of my graduate-school experience almost since the beginning. I joined the project as part of the inaugural group of NULab fellows in the spring of 2013. I remember sitting around a table with the other fellows, hearing about all the different projects we might be assigned to, and thinking, “I really hope the spots for that newspaper project don’t fill up before I get to choose.” Thankfully, they didn’t. The NULab fellows’ role has changed since then, but I’ve always been able to stay attached to the project, and I’m so grateful.
Viral Texts is one of the defining pieces of my graduate school experience. It shaped my understanding of digital humanities, and it stretched me to work in multiple disciplines. It taught me how to work with a team while keeping my individuality. And I learned an awful lot about how nineteenth-century newspapers work.
And now, in true Viral Texts fashion, it’s time for me to pass on the scissors and the quill. Starting in May, I’ll be joining the research division at the Roy Rosenzweig Center for History and New Media at George Mason University. I’ll be working with PressForward, Zotero, and mostly Tropy, CHNM’s new Mellon-funded project for archiving and organizing photos. I’m particularly excited about working with Tropy, though I’m a little bummed that my dissertation will (I hope) be close to complete before Tropy is ready for the big time.
The projects and tools at CHNM were my first encounter with digital humanities, even before I wanted to embrace the digital in my own work. Throughout my graduate career, I’ve benefited greatly from Zotero and Omeka and other amazing work at the center, and I’m looking forward to helping develop other great tools for myself and others to use.
In joining CHNM and departing Viral Texts, I take these words from the valedictory editorial of Thomas Ritchie, editor of the Richmond Enquirer: “I cannot close this hasty valedictory, without again expressing the sentiments of gratitude and affection with which I am so profoundly penetrated.” So to everyone on the team—Ryan, David, and Fitz in particular—thanks. It’s been great.
If you read my last post, you know that this semester I engaged in building a Bookworm using a government document collection. My professor challenged me to try my system for parsing the documents on a different, larger collection of government documents. The collection I chose to work with is the Official Records of the Union and Confederate Navies. My Barbary Bookworm took me all semester to build; this Civil War navies Bookworm took me less than a day. I learned things from making the first one!
This collection is significantly larger than the Barbary Wars collection—26 volumes, as opposed to 6. It encompasses roughly the same time span, but 13 times as many words. Though it is still technically feasible to read through all 26 volumes, this collection is perhaps a better candidate for distant reading than my first corpus.
The document collection is broken into geographical sections, the Atlantic Squadron, the West Gulf Blockading Squadron, and so on. Using the Bookworm allows us to look at the words in these documents sequentially by date instead of having to go back and forth between different volumes to get a sense of what was going on in the whole navy at any given time.
Process and Format
The format of this collection is mostly the same as the Barbary Wars collection. Each document starts with an explanatory header (“Letter to the secretary of the navy,” “Extract from a journal,” etc.). Unlike BW, there are no citations at the end of each document. So instead of using the closing citations as document breakers, I used the headers. Though there are many different kinds of documents, the headers are very formulaic, so the regular expressions to find them were not particularly difficult to write. 1
Further easing the pain of breaking the documents is the quality of the OCR. Where I fought the OCR every step of the way for Barbary Bookworm, the OCR is really quite good for this collection (a mercy, since spot-checking 26 volumes is no trivial task). Thus, I didn’t have to write multiple regular expressions to find each header; only a few small variants seemed to be sufficient.
The high quality OCR enabled me to write a date parser that I couldn’t make work in my Barbary Bookworm. The dates are written in a more consistent pattern, and the garbage around and in them is minimal, so it was easy enough to write a little function to pull out all parts. In the event that certain parts of the dates were illegible, or non-existent, I did make the function find each part of the date in turn and then compile them into one field, rather than trying to extract the dates wholesale. That way, if all I could extract was the year, the function would still return at least a partial date.
Another new feature of this Bookworm is that the full text of the document appears for each search term when you click on the line at a particular date. This function is slow, so if the interface seems to freeze or you don’t seem to be getting any results, give it a few minutes. It will come up. Most of the documents are short enough that it’s easy to scroll through them.
Testing the Bookworm
Some of the same reservations apply to this Bookworm as I detailed in my last post about Barbary Bookworm—they really apply to all text-analysis tools. Disambiguation of ship names and places continues to be a problem. But many of the other problems with Barbary Bookworm are solved with this Bookworm.
The next step that I need to work on is sectioning out the Confederate navy’s documents from the Union navy’s. Right now, you can get a sense of what was important to both navies, but not so easily get a sense of what was important to just one side or the other.
To be honest, I don’t really know enough about the navies of the Civil War to make any significant arguments based on my scrounging around with this tool. There are some very low-hanging fruit, of course.
Particularly since I don’t do Civil War history, I’d welcome feedback on both the interface and the content here. What worked? What didn’t? What else would you like to see?
Feel free to send me questions/observations/interesting finds/results by commenting on this post (since there’s not a comment function on the Bookworm itself), by emailing me, or for small stuff, pinging me on Twitter (@abbymullen). I really am very interested in everyone’s feedback, so please scrub around and try to break it. I already know of a few things that are not quite working right, but I’m interested to see what you all come up with.
- Ben had suggested that I do the even larger Civil War Armies document collection; however, that collection does not even have headers for the documents, much less citations, so the document breaking process would be exponentially more difficult. It’s not impossible, but I may have to rework my system—and I don’t care about the Civil War that much. However, other document collections, such as the U.S. Congressional Serial Set, have exactly the same format, so it may be worth figuring out. ↩
This past semester, I took a graduate seminar in Humanities Data Analysis, taught by Professor Ben Schmidt. This post describes my final project. Stay tuned for more fun Bookworm stuff in the next few days (part 2 on Civil War Navies Bookworm is here).
In the 1920s, the United States government decided to create document collections for several of its early naval wars: the Quasi-War with France, the Barbary Wars, and the Civil War (the War of 1812 did not come until much later, for some reason). These document collections, particularly for the Quasi-War and the Barbary Wars, have become the standard resource for any scholar doing work on these wars. My work on the Barbary Wars relies heavily on this document collection. The Barbary Wars collection includes correspondence, journals, official documents such as treaties, crew manifests, other miscellaneous documents, and a few summary documents put together in the 1820s. 1
It’s quite easy to get bogged down in the multiplicity of mundaneness in these documents—every single day’s record of where a ship is and what the weather is like, for instance. It’s also easy to lose sight of the true trajectory of the conflict in the midst of all this seeming banality. Because the documents in the collection are from many authors in conversation with each other, we can sometimes follow the path of these conversations. But there are many concurrent conversations, and often we do not have the full correspondence. How can we make sense of this jumble?
- U.S. Office of Naval Records and Library, Naval Documents Related to the United States Wars with the Barbary Powers (Washington: U.S. Govt. Print. Off., 1939); digitized at http://www.ibiblio.org/anrs/barbary.html. ↩
This past week in my Humanities Data Analysis class, we looked at mapping as data. We explored ggplot2’s map functions, as well as doing some work with ggmap’s geocoding and other things. One thing that we just barely explored was automatically extracting place names through named entity recognition. It is possible to do named entity recognition in R, though people say it’s probably not the best way. But in order to stay in R, I used a handy tutorial by the esteemed Lincoln Mullen, found here.
I was interested in extracting place names from the data I’ve been cleaning up for use in a Bookworm, the text of the 6-volume document collection, Naval Documents Related to the United States Wars with the Barbary Powers, published in the 1920s by the U.S. government. It’s a great primary source collection, and a good jumping-off point for any research into the Barbary Wars. The entire collection has been digitized by the American Naval Records Society, with OCR, but the OCRed text is not clean. The poor quality of the OCR has been problematic for almost all data analysis, and this extraction was no exception.
The tutorial on NER is quite easy to follow, so that wasn’t a problem at all. The problem I ran into very quickly was the memory limits on my machine–this process takes a TON of memory, apparently. I originally tried to use my semi-cleaned-up file that contained the text of all 6 volumes, but that was way too big. Even one volume proved much too big. I decided to break up the text into years, instead of just chunking the volumes by size, in order to facilitate a more useful comparison set. For the first 15 years (1785-1800), the file was small enough, and I even combined the earlier years into one file. But starting in 1802, the file was still too large even with only one year. So I chunked each year into 500kb files, and then ran the program exactly the way the tutorial suggested with multiple files. I then just pushed the results of each chunk back into one results file per year.
Once I got my results, I had to clean them up. I haven’t tested NER on any other type of document, but based on my results, I suspect that the particular genre of texts I am working with causes NER some significant problems. I started by just doing a bit of work with the list in OpenRefine in order to standardize the terrible spelling of 19th-century naval captains, plus OCR problems. That done, I took a hard look at what exactly was in my list.
Here’s what I found:
1. The navy didn’t do NER any favors by naming many of their ships after American places. It’s almost certain that Essex and Chesapeake, for instance, refer to the USS Essex and USS Chesapeake. Less certain are places like Philadelphia, Boston, United States, and even Tripoli, which are all places that definitely appear in the text, but are also ship names. There’s absolutely no way to disambiguate these terms.
2. The term “Cape” proved to be particular problems. The difficulty here is that the abbreviation for “Captain” is often “Cap” or “Capt,” and often the OCR renders it “Cape” or “Ca.” Thus, people like Capt. Daniel McNeill turn up in a place-name list. Naval terms like “Anchorage” also cause some problems. I guarantee: Alaska does not enter the story at all.
3. The format of many of these documents is “To” someone “from” someone. I can’t be certain, but it seems like the NER process sometimes (though not always) saw those to and from statements as being locational, instead of relational. I also think that journal or logbook entries, with their formulaic descriptions of weather and location, sometimes get the NER process confused about which is the weather and which is the location.
4. To be honest, there are a large number of false hits that I really can’t explain. It seems like lists are particularly prone to being selected from, so I get one member of a crew list, or words like “salt beef,” “cheese,” or “coffee,” from provision lists. But there are other results as well that I just can’t really make out why they were selected as locations.
Because of all these foibles, each list requires hand-curation to throw out the false hits. Once I did that, I ran it through R again to geocode the locations using ggmap. Here we also had some problems (which I admittedly should have anticipated based on previous work doing geolocation of these texts). Of course, many of the places had to be thrown out because they were just too vague to be of any use: “harbor,” “island,” and other such terms didn’t make the cut.
When I ran the geocoder for the first time, it threw a bunch of errors because of unrecognizable place names. Then I remembered: this is why I’ve used historical maps of the area in the past–to try to track down these place names that are not used today. Examples include “Cape Spartel,” “Cape DeGatt,” and “Cape Ferina.” (I’m not sure why they were all capes.) I discovered that if you run the “more” option on the geocode, the warnings don’t result in a failed geocode, plus all the information is useful to get a better sense of the granularity of the geocode, and what exact identifier the geocoder was using to determine the locations.
This extra information proved helpful when the geocoded map revealed oddities such as the Mediterranean Sea showing up in the Philippines, or Tunis Bay showing up in Canada. Turns out, the geocoder doesn’t necessarily pick the most logical choice for ambiguous terms: there is, in fact, an Australasian sea sometimes known as the Mediterranean Sea. These seemingly arbitrary choices by the geocoder mean that the map looks more than a little strange.
So what’s the result here? I can see the potential for named-entity extraction, but for my particular project, it just doesn’t seem logical or useful. There’s not really anything more I can do with this data, except try to clean up my original documents even more. But even so, it was a useful exercise, and it was good practice in working with maps and data in R.
Last week, an opinion piece appeared in the New York Times, arguing that the advent of algorithmically derived human-readable content may be destroying our humanity, as the lines between technology and humanity blur. A particular target in this article is the advent of “robo-journalism,” or the use of algorithms to write copy for the news. 1 The author cites a study that alleges that “90 percent of news could be algorithmically generated by the mid-2020s, much of it without human intervention.” The obvious rebuttal to this statement is that algorithms are written by real human beings, which means that there are human interventions in every piece of algorithmically derived text. But statements like these also imply an individualism that simply does not match the historical tradition of how newspapers are created. 2
In the nineteenth century, algorithms didn’t write texts, but neither did each newspaper’s staff write its own copy with personal attention to each article. Instead, newspapers borrowed texts from each other—no one would ever have expected individualized copy for news stories. 3 Newspapers were amalgams of texts from a variety of sources, cobbled together by editors who did more with scissors than with a pen (and they often described themselves this way). Continue reading On Newspapers and Being Human
- The article also decries other types of algorithmically derived texts, but the case for computer-generated creative fiction or poetry is fairly well argued by people such as Mark Sample, and is not an argument that I have anything new to add to. ↩
- This post is based on my research for the Viral Texts project at Northeastern University. ↩
- In 1844, the New York Daily Tribune published a humorous story illustrating exactly the opposite, in fact—some readers preferred a less human touch. ↩
[This semester I’m taking Humanities Data Analysis with Professor Ben Schmidt. One of our tasks for this week was to build a random-walk generator using 3-grams. Here’s my quick writeup of my generator cross-posted from our course blog.]
We’ve been reading a lot of fairy tales around my house recently, so I wanted to see how well-spun of a tale I could create by walking randomly through a collection of fairy tales. I selected four fairy-tale collections from Project Gutenberg to test this idea on. Code is on GitHub.
I selected these four collections:
- The Thousand and One Nights, Volume 1
- The Blue Fairy Book (by Andrew Lang)
- Fairy Tales of Hans Christian Anderson
- The Fairy Tales of Charles Perrault
The addition of the Arabian Nights stories to Western European fairy tales makes the random generator more interesting, sometimes throwing the geographical sense of the tale into a different place and a different world.
This version generated my favorite beginning: “once upon a time a man by the river yes he was looking straight into the deep waters skeletons of walruses.”
But other versions of the generator took an even darker turn. Here’s the raw text:
“once upon a great procession which was conscious of pain And sore regret of which she said nothing but torment and affliction that He sniffed about to give the ants were always running to and when he approached her they did not really birds but she bore thee Thou hast nothing to me Only tell me something Why this is what you say What is the news O my sister relate to me Art thou she whom he found it impossible to think of The old rough doll You are learned and wise men assembled together in his age and to nail up my mind every earthly care and sorrow with soft turf From the narrow walks and the Wezeer the father of Is both of you should care so much that renders men sinful and impure He fully realized the true the speaker s hand saying to each other till the morning following I have with me from first to last and then burst and fell fast asleep”
And here’s the story, with some punctuation that I added for “clarity”:
Once upon a great procession–which was conscious of pain and sore regret, of which she said nothing but torment and affliction that He sniffed about to give. The ants were always running to, and when he approached her, they did not really birds but she bore thee: “Thou hast nothing to me. Only tell me something: Why this is what you say? What is the news? O my sister relate to me! Art thou she whom he found it impossible to think of? The old rough doll? You are learned, and wise men assembled together in his age and to nail up my mind every earthly care and sorrow with.” Soft turf from the narrow walks and the Wezeer the father of Is, both of you should care so much! That renders men sinful and impure. He fully realized the true the speaker’s hand, saying to each other till the morning following, “I have with me from first to last,” and then burst and fell fast asleep.
And sometimes it’s important to be reminded of where your texts come from. I didn’t remove any text at all from the Project Gutenberg texts, which means that the copyright and distribution information could appear in our stories too. For example:
“The two grand annual festivals are observed with public domain eBooks Redistribution is subject to particular laws or rules with respect to our beetle to himself but the observance of this Wezeer So the porter approached the Distracted Slave of Love when his boat or playing in the lap of prosperity and the fear of him said the Fire drum Peter has gone away I ll do something in me.”
I might publish a longer generated story sometime soon, but all this generator proves is that tales can be wiggly indeed.
After an AHA in which I heard a lot about how digital history needs to be about results as well as methodology, I decided to write up a post about the results I gained from mapping the Quasi-War. Special h/t to Cameron Blevins and Yoni Appelbaum for inspiring me to write about my research. I’m also using Yoni’s hyperlink-style citations.
For my seminar in Empires and Colonialism this past semester, I wrote about the United States’ Quasi-War with France. The paper argues that the Quasi-War was one of the United States’ first chances to engage with international law on a broad scale, and that the conflicting legal realities of an undeclared war helped to destabilize the French empire in the Caribbean to the breaking point. As part of that seminar paper, I mapped encounters between the French and the Americans (with a few British encounters) from 1797 to 1800. This map proved to be more illuminating than I expected, and it became an integral part of my argument about the primacy of prize courts in the Quasi-War. The map has clickable points where encounters occurred, as well as a fuller explanation of the judgments I made in creating it. You can see the map here. What follows is my explanation of what the map does.
From 1798 to 1800, the United States waged an undeclared maritime war with France. Though this conflict is often described as a naval war, it was not a traditional one. Almost no French naval vessels entered the Caribbean, and the hostile encounters between the French and the Americans were almost all battles between privateers and merchant vessels.
Why were there so many privateers in the Caribbean? Geographically, the islands had always been prime areas for piratical types—lots of inlets and tiny islands for staging. In addition, the privateers served an important role in providing for the colonies. The dominance of the sugar industry had restricted the colonies’ ability to provide basic foodstuffs for their people, both white and black. Previous to this conflict, the United States had provided a large portion of the colonies’ food for the sugar workers—one scholar states that St. Domingue relied on the commerce of at least 600 American ships for basic supplies during 1796 alone. But as a result of the non-intercourse act, the supply had dried up. Consul Turell Tufts wrote in despair to President Adams about the port of Cayenne: “Every exertion is making there in Privateering, as they consider it the very harvest of Plunder; and besides, they have no other means of procuring Supplies.”
Constant war with Britain meant that supplies from elsewhere in the empire were difficult to come by, so taking supplies that were already present made perfect sense. When the privateers captured merchant vessels in the Caribbean, they were able to bring in both the money from the sale of the vessel and cargo, and also parts of the cargo itself. The colonial governments had a vested interest in the actions of the privateers as well—not just because of the food itself, but also because of the “consequent discontent” if food was not available. In an already volatile political environment, maintaining order sometimes meant encouraging the privateers.
It’s not surprising that the United States government decided that the French were a threat enough to build a navy. Compared to the number of captures by Barbary corsairs, the French threat was immense and widespread. There’s no way to know with any certainty how many captures actually occurred, but given the number of captures that we do have information about (more than 250 captures with enough spatial information to be plotted on a map, and hundreds more with no spatial data), the total number of captures could easily range over a thousand. When the navy did finally make it to the Caribbean, its commanders adopted strategies that helped them to deal with the huge numbers of privateers in the area. Recognizing how privateers operated, the commanders planned their locations and logistics accordingly.
At the heart of both the privateers’ and the navy’s strategy was the prize court. International maritime law had established the prize court as the appropriate way to adjudicate the legal claims of captor and captured alike. 1 Privateers, by and large, adhered to the prize-court structure; at least, the claims of piratical behavior were much less frequent than accounts of lawful prize-taking. This is not to say that every prize brought into a prize court was fairly and impartially adjudicated: privateers could count on certain ports as friendly to their causes, where the commissioners would declare captures lawful prize on the slightest provocation.
Though French privateers made captures all across the world, they found the greatest success in the Caribbean. Privateers could use some of the same tactics as the famed pirates of the Caribbean, using sheltered harbors and small islands as protection and cover. But privateers differed from pirates in that privateers needed to stay close to the ports where they could send in prizes, whereas pirates tended to plunder their captures. The abundance of colonial governments in the Caribbean meant an abundance of prize courts.
Privateers’ vessels weren’t large enough to sustain long periods at sea, and captures only reduced the time they could spend at sea. Privateers elongated their time at sea by placing prize crews on board captures and sending the prizes unaccompanied into port. These prizes were less likely to actually bring the captors their prize money, since the chances of the prize making it unscathed into port decreased when the privateer did not escort the prize back. In addition, the prize crew was taken from the crew of the privateer, which meant that even this solution would eventually leave the privateer with too few men to maneuver effectively.
The majority of captures were within a few days’ sail of a prize court. For French privateers, French ports were the ideal, but other neutral ports (such as Curacao) would do in a pinch. British ports were, of course, out of the question, as the British were at war with the French. At the beginning of the war, ports in Guadeloupe (particularly Basseterre) and Saint-Domingue were most likely to condemn American prizes. As the war progressed, and the Americans negotiated trade agreements with Toussaint separate from the French government, Guadeloupe became the primary port where American prizes would likely be condemned.
Prize courts—or rather, accessibility to prize courts—also dictated American strategy against the privateers. For a navy being literally built ship by ship, one-on-one pitched battles against the privateers could never be a feasible strategy. Instead, the naval commanders focused their attention on the prize court ports: places they could be sure to encounter privateers, and even more frequently, their prizes. This strategy had two strengths: first, it gave the navy a better chance of actually capturing privateers, and thus removing their threat. But second, it also made privateering less profitable even for the privateers who eluded capture. Prizes were relatively easy to capture, since they had a skeleton crew of belligerents along with the original crew, who were all too willing to rise up against the prize crew. And if those prizes never made it into port, all the cost in munitions, time, and crew members that the privateer had expended was meaningless. No prize court, no matter how lenient, would condemn a vessel whose papers never made it to port. Though these reasons were never spelled out in so many words, they must have occurred to at least some of those men who handed down orders.
The Americans adopted a strategy, then, that kept them very close to enemy ports. They targeted Guadeloupe specifically—a whole squadron was ordered to stay “in the neighborhood of Guadeloupe,” as the secretary of the navy had put it. They were then able to capture ships in neutral waters as they came in and out of those ports. On occasion, American naval vessels came very close to violating neutral waters: international law declared that water within a cannon-shot of land was the territory of the nation that held the land. But no one ever objected to their captures on those grounds. The naval vessels maintained an even smaller range than the privateers. Privateers usually made their captures within two or three days’ sail of a prize court; the navy maintained a distance of one day or less.
The number of naval vessels on the Guadeloupe station at any one time vacillated wildly. The secretary of the navy attempted to keep at least half a dozen ships there, but maintenance needs, expiration of terms of enlistment, sickness, or any number of other factors could pull ships off their patrolling grounds. Once Toussaint began to request the use of American naval vessels to help his cause in Saint-Domingue, the number of ships at Guadeloupe was even more unpredictable. And of course, individual captains sometimes took their ships off to places outside the strategic area for convoy duty or by sheer incompetence.
American naval vessels could not maintain perpetual patrols off Guadeloupe, no matter how ideal the circumstances. Just like the privateers, they needed a safe place to go for supplies, maintenance, and prize adjudication (they too operated on the prize system). They primarily used St. Kitts, to the north of Guadeloupe, as a base for resupply. However, prices in the islands were exorbitant, so the secretary of the navy sent supply vessels from the United States as well.
These geographical constraints did not preclude the navy’s sailing elsewhere—far from it. But the number of captures very near to enemy ports indicates that the navy’s strategy was effective. By the end of the year 1800, Thomas Truxtun, who was cruising off Guadeloupe, wrote to Thomas Tingey, “With all this cruising my success has been very limited indeed, for the french have become scarce, so much so, that what I formerly found (chasing) an amusement, and pastime, is now insiped, Urksome & tiresome.”
In fact, by this time the treaty had already been signed to reestablish commercial relations between the United States and France, though it would be another several months before the terms were ratified by all parties. Michael Palmer estimates that U.S. naval forces, averaging 16 ships at any given time between 1798 and 1800, captured 86 privateers over the course of the war. 2 This number is impressive for such a small force, but it still doesn’t come even close to an annihilation of the privateer forces. Many factors contributed to the eventual decline of French privateering, but it does seem that targeting the prize courts was one of those factors.The American naval strategy had succeeded.
- For more about prize law and its relationship to empire, see chapter 3 of Lauren A. Benton, A Search for Sovereignty: Law and Geography in European Empires, 1400–1900 (Cambridge; New York: Cambridge University Press, 2010). ↩
- Just as we don’t have enough spatial data to map all of the French victories over American shipping, we also don’t have enough spatial data to map these American victories completely—again, the map shows about ¼ of these victories. ↩
Today is Ada Lovelace Day, honoring a woman who is often credited with being the first computer programmer because of her work programming for Charles Babbage’s Analytical Engine in the 1840s. The day honors Ada and all women who are involved in science, technology, engineering, and mathematics.
I am not a woman in a STEM field, not really. But I am celebrating Ada Lovelace Day today because I am the humanities scholar I am through the influence of a woman who did work in STEM—my mom. So I’d like to celebrate Ada Lovelace Day 2014 by honoring my mom.
My mom was an elementary school teacher for the first part of her adult life. Once she had kids, she transitioned to writing elementary-school textbooks for a small press, a role she maintained for the rest of her life. Though she worked on a variety of projects, her favorite, and her longest-running project, was the elementary science curriculum. Writing these textbooks gave her the chance to incorporate into a curriculum the experiments and explorations she had always done with us kids at home. We got to look at eclipses through little holes in paper, and collect animal tracks using plaster. We were always being subjected to discussions about how best to demonstrate viscosity, or the most interesting way to talk about the distance between planets, or the kid-friendliest way to learn about civil engineering. And all of these household discussions worked their way into her textbooks.
I didn’t always appreciate my mom’s emphasis on science and mathematics. I used to cringe when she’d give me two similar items in the grocery store and ask me to figure out which one was the better deal, based on their price and weight (this was before the stores so helpfully printed the “unit price” on the price tag). Or she would play games with me to estimate how much our total grocery bill would be based on my having to keep track of all the items’ prices in my head.
When I was a teenager, I worked for the same press as my mom, and though I worked in a different department, I sometimes got outsourced to her as a researcher and writer. She gave me a vast array of different assignments, like writing about autonomous underwater vehicles, or atmospheric optics, for a call-out page in a 5th-grade science textbook. Initially I wasn’t that excited about some of the topics, but I ended up catching her enthusiasm and digging in.
In a few weeks, it will be the fourth anniversary of my mom’s death. She was able to finish the entire elementary school science curriculum before becoming too sick to work. That’s one scientific legacy. But the legacy is more personal, too. I still find myself wishing I could call her when I see things like halos around the sun, or an oddly colored insect, because I know that she would most appreciate the beauty of a random scientific phenomenon.
My whole family has been inspired by my mom’s legacy. In fact, of four kids, I’m the only one who doesn’t have some sort of higher education in a STEM field. Three of the four of us are working on PhDs (and the fourth is still in college—the bar’s pretty high, Auria…). My latent mathematician has been coming out recently as I get into digital humanities, but the very way I think about knowledge and research–even as a historian—comes from my mom. Both of my parents have always encouraged us to educate ourselves, both officially and unofficially. Both my mom and my dad have always pushed us to excel as far as we can, while supporting us along the way. But today, Ada Lovelace Day, I want to honor the one woman in a STEM field who has meant the most to me and has shaped my life more than anyone else. I love you, Joyce Garland. You’re the best role model I could ever have.