Timeline of the Life of Eva Baen

As an educator, I occasionally run into a problem: tunnel vision. Sometimes, I get so excited telling a story that I forget to paint the larger context, or I put so much time into setting up causes, influences, effects, etc, that I forget to include the smaller details that bring the subject to life. My final project for Digital History is an attempt to supplement this issue.

Using Timeline JS, I constructed a timeline of the life of Eva Baen, the young immigrant who acts as the centerpiece of the program I coordinate at NMAJH, Becoming American: History of Immigration 1880s-1920s. (Note: this is not an official Museum resource, but I hope to eventually bring it to that standard for that purpose.)

In this timeline, I juxtapose events from Eva’s life, and her family’s, with major events from US and Russian history. That way, teachers would be able to use this timeline to supplement their classroom lesson and tour with whatever isn’t covered, as well as relate the program to other subjects that aren’t included in the tour at all, such as the Great Depression and the New Deal.

Timeline entries are divided into four categories: Eva’s life, her family, US history, and Russian history. This was partially inspired by the Patriots & Pirates exhibit at the Independence Seaport Museum, where different colored ropes representing France, the U.S., Great Britain, and the Barbary States show how these different nations interacted over time and through various conflicts, like the War of 1812. This is replicated with the horizontal bars along the bottom of the timeline, as well as the background color of each timeline entry: purple, green, blue, and red, respectively. This makes it easier to distinguish between different entries from a glance.

Additionally, the juxtaposition of events makes it easier to come to certain conclusions that are much more compelling than simply being told about them in a classroom. For example, students can see the date when Leon and Bessie came to the U.S. just before the Johnson-Reed Act and hypothesize that, without other information and knowing that Eva’s parents make it to America eventually, that the parents probably came with Leon and Bessie in 1921.

Those already familiar with Becoming American will notice that there are a lot of artifacts left out of the timeline that are usually covered in the lesson. Only two artifacts from the lesson are included: Eva’s family portrait and one of her attendance cards. This choice was made for three reasons. First, a lot of the artifacts cover the same parts of Eva’s life. We have many school-related artifacts, for example, and including them all will lead to a very lopsided timeline. Second, my department is currently putting together digital resources on a variety of platforms, such as Smithsonian Learning Labs. Those are much better used to display all of the artifacts than the timeline would be. Third, this gives me a chance to show other resources that are not included in the program, such as census records, and relevant pop culture to which connections can be made, such as the animated film An American Tail.

Additionally, many of the timeline entries contain links to relevant resources, particularly lesson plans. These were found from organizations like the Library of Congress and PBS, and are primarily suited for middle and high school students. One of the downsides to using a timeline is that entries must be organized by specific dates or date ranges, which makes it difficult to learn about subjects that are not necessarily so clear-cut. Several of these lesson plans, such as one on photography during the Great Depression, continue Becoming American‘s focus on material culture as learning tools. Entries also link to the growing genre of educational series on Youtube, such as Crash Course: the intention is to connect these materials with resources that students may already be familiar with.

On its own, this timeline does not seek to act as an educational tool itself, and in fact includes little more than bare bones about Eva Baen’s life. This is partially because many stories from her life are not tied to a specific date, and because that is the purpose of Becoming American, not this timeline. Instead, this timeline is meant to serve as a supplemental resource, a way of looking at this subject from a different perspective, and a gateway into a variety of connected subjects that can be further explored in class rather than at NMAJH.


Imagining a Better Wikipedia

A few months ago at PubComm, on a whim, I attended a workshop titled “Avoiding the Seven Deadly Sins of Wikipedia: Understanding and Working with Wikipedia Culture” by Mary Mark Ockerbloom, the Wikipedian-in-Residence at the Chemical Heritage Foundation. While I had only added to an informal school-specific Wikipedia once, for a class, I was swept up in her descriptions of writing and citation standards, the vigilance of power users, and especially the unbalanced demographics of Wikipedia users and how that affects what topics are covered, and to what extent, on the website. Ockerbloom showed statistics about how, for example, 85-90% of Wikipedia users who indicate their gender are men. The number of articles about men vs other genders, and their respective lengths and depths, reflects this.

I think this issue is a prominent one in any crowdsourcing project: how do users affect what work is being done? To be fair, this is an issue with any project: people will naturally want to do work that is more relevant or interesting to them. However, on the larger scale of crowdsourcing, these kinds of biases become more apparent. How does this get fixed, to make sure that the products of Wikipedia editing are distributed more evenly?

One solution is through Wikipedia edit-a-thons, such as through the Wikipedia Rewriting Project, which organizes drives to write about underrepresented topics, such as women and people of color. These kinds of events have raised amazing traction in the past few years and have contributed to a wealth of new articles being added to Wikipedia, however this is ultimately a small dent. Could there be another model for this, beyond simply encouraging people to write about underrepresented topics more or gaining more women and POC users?

I was struck by the simplicity of other crowdsourcing projects, such as Building Inspector. Through this site which seeks to improve map-reading AI, users can identify colors on a map, fix footprints, and transcribe addresses. Users choose the task they want to do, and the website presents a small area of a map for users to complete the task. The website automatically produces different areas of the map, so user preference for map location does not factor into the work being done. It’s a fairly mindless activity that users can click through and make an impact on the digital humanities without much consideration or energy.

Can some of this function be translated to Wikipedia? This could perhaps be done with minor edits, such as proofreading and finding citations. A user could log on and be presented with a random paragraph or “[needs citation]” marker. The user could then proofread the paragraph for comprehension, or attempt to find a citation for the claim. This would especially make it easy for more people to contribute, especially those who do not have the time, energy, or knowledge to edit Wikipedia more fully. However, this would take much more energy than simply clicking on a map, and of course does not solve the issue of submitting content in the first place.

Of course, the problem of Wikipedia containing a significantly larger portion of articles about white men is much larger than just Wikipedia: the patriarchy, white supremacy, and other forms of oppression all play a role in current Wikipedia users both being and writing primarily about white men. These hegemonies must ultimately be dismantled, but in the meantime, let’s all go join edit-a-thons!

Web Map Project: Visualizing Immigration

For my map project, I created a Google Maps that shows some of the major ports for Eastern European immigrants at the turn of the 20th century, as well as some of the larger departure ports in Europe. While simple, I think that this is a useful way of looking at topics relating to immigration, considering that immigration is all about movement: using a map helps people to visualize this more easily.

I used Google Maps primarily because I’m already familiar with it, and because most potential users would be too. I think this is an important consideration when creating digital projects, in terms of increasing ease of access. While this creates a feedback loop of sorts (where users are losing an opportunity to learn about new technologies) and Carto has different features, for the purposes of my map, I would want users to focus on analyzing the content rather than having to wade through learning how to use a different site, such as the beautiful but opaque The Knotted Line. (Also, to be honest, I had a lot of difficulties navigating Carto’s website!)

I think one of the biggest takeaways from this map is how large immigrant populations correspond with geographic convenience. It makes perfect sense that huge populations of immigrants lived in East Coast cities like New York and Philadelphia because those are the ports that they enter. However, this map helps to demonstrate why those East Coast ports were so popular: because the distances make sense to go from Latvia, to England/Netherlands, to America’s NE coast. One can also see why other programs, like the Galveston Plan, were so important in terms of redistributing immigrants throughout the rest of the country.

Taking this project further, I would be interested in incorporating train lines that brought immigrants to populous inland cities, such as Chicago. How far west did immigrants settle? It would be interesting to compare this with Asian immigrants entering through Angel Island and other West Coast ports, although legislative restrictions such as the Chinese Exclusion Act of 1882 and the Johnson-Reed Act of 1924 directly impact the number of incoming immigrants. It would also be interesting to see how these numbers and paths change over time, similar to the New York Times’s Immigration Explorer.

100 Years Later: Making Statistics Usable Again

Last semester, I wrote a historiography on Jewish immigration to the U.S. at the turn of the 20th century, a period that is typically defined as ranging from 1881 to 1924. During this time, approximately 23 million immigrants came to this country, about 2 million of which were Jews from Eastern Europe.

While researching books to write about, I found a fascinating dissertation (political science PhD at Columbia University) on the subject: Jewish immigration to the United States from 1881 to 1910 by Samuel Joseph, notable because it was written in 1914, while mass immigration was still happening!

In his dissertation, Joseph took a primarily statistical approach, quantifying newcomers into categories based on things like country of origin, destination, port of entry, occupation, and even literacy levels.

When I read this book last year, I realized that this information might be really useful in  my work at NMAJH. However, while this book has been digitized, Joseph’s statistics are presented only in chart form, which is difficult both to interpret and to feed through an OCR.

Destination of Jewish Immigrants, 1899 to 1910, by division. The percentages help to visualize the difference in these numbers, but can this be improved?

So for his assignment, I wanted to see how I could make these statistics more useful, both to visualize them, and to make them more convenient than having to scroll through a slowly-loading 200 page book on Hathi Trust. I made the following charts through Excel, which I had to learn how to do last year to create board reports at work. Here are a few reasons why I love Excel:

  1. You can make a variety of charts super quickly.
  2. You can manipulate/fix the data and see how that changes your chart.
  3. It’s really easy to customize how the chart looks, such as making colors easier to read, including data values, etc.
Here is that same chart, which I translated into a pie chart. Now it’s a little easier to quickly see that same distribution! If I had a little bit more specific information (such as how he’s defining these divisions), I would consider a heat map of the U.S. that shows people’s destinations.


These show the number of Jews with certain occupations in the Russian Empire in 1897, and then what percentage that was of the total amount of people with those occupations. So even though the most Jews work in manufacturing, those Jews only occupy 10% of total manufacturing jobs. I tried to fit these two data sets into a single chart but couldn’t figure out how to do it without having two axis or simply labeling the first bar chart with the percentages.

This line graph shows, over time, how many immigrants were arriving in Philadelphia from each country. A footnote on the graph explains that, in 1891, Austria-Hungary and Roumania [sic] were counted as “Others.”
I think it could prove useful to eventually continue doing this for the rest of the book, or at least transcribing Joseph’s charts into plain text. If possible, that would involve checking his statistics against his original sources, which come from a variety of places (census records, synagogue and charity reports, etc), so this information can be made more consistent and to clear up any uncertainties.

Harsh warnings: a digital history review

New semester, new reason to use this blog! I will primarily be writing for my “Digital History” class, but I will try to write some extracurricular blog posts as I remember/am able.

It’s hard to engage with anything, much less history, while ignoring current events since the inauguration. Suddenly every topic or historical theme I read about seems like either a failed warning of what’s passed, an omen of what’s to come, or an unfair distraction. Some of these warnings or omens are coincidental, like stumbling across a documentary with a timely subject, or deliberate.

One such example of a deliberate omen is the Twitter account @Stl_Manifest, launched by the software developer Russel Neiss and Rabbi Charlie Schwartz as a direct response to Donald Trump’s executive order that places extreme limits on travel and immigration from certain countries in the Middle East, all of which have predominantly Muslim populations. The executive order most severely affects Syrian refugees, all of whom had to go through about two years of intense vetting before being granted entry into the country. The poignancy of this account comes from Trump having signed this executive order on Holocaust Remembrance Day.

The twitter account is simple: it spits out tweets that name a passenger of the St. Louis, explain that the US turned them away at a time in need, and then identify the death camp or other location where each individual was eventually murdered by Nazis or their supporters. A simple bot most likely assembled these tweets from a database from the US Holocaust Museum (USHMM), filling in as much information as was available, such as photographs of the victim. The account, which was only active on the day Trump signed the executive order (Jan 27), generated 252 tweets, most with several hundred retweets and likes.

Evaluating this project depends on the goals. As a tool to learn about the St. Louis and its passengers, the site gives little information. The profile links to USHMM’s encyclopedia article about the ship’s story, but that’s it. We also learn only their name, location of death, and occasionally what they looked like. This risks objectifying the passengers, seeing them only as their deaths. This runs counter to the approaches taken by other Holocaust museums, such as the Museum of Jewish Heritage, which strives to highlight and enliven the lives victims had before the Holocaust.

However, the goal of this project is not to learn about the St. Louis but to spread awareness of it, and of the US’s history of denying refugees. This account was everywhere on Friday. Everyone was retweeting this account, evoking this history while condemning the executive order. Neiss and Rabbi Schwartz were not the only ones to make this connection; major publications such as The Washington Post and The Guardian wrote articles evoking the ship’s story. However, Twitter’s condensed format lends itself to the goal of spreading awareness: it is much quicker and easier to share a single tweet than it is to read an entire news article. The tweets slip easily into one’s Twitter feed, whereas a news article linked to in a Twitter or Facebook feed would need to be opened into a separate tab. Additionally, the 140-character limit forces the tweets to be blunt and harsh, and paired with the first person language, each tweet’s message is succinct and tragic.

Twitter also lends itself to easy engagement with these stories. Many individual tweets had direct replies or were quoted, with other users sharing additional information, emphasizing the tragedy of the St. Louis, or reflecting on the timeliness of the account. However, this also offers the opportunity for users to respond inappropriately, such as commenting on the demographics of those the Nazis murdered or asserting misinformed arguments about contemporary refugees and terrorism. Twitter does not allow for irrelevant or distracting comments to be deleted, but even on a different platform, where are the limits of acceptance and curation? Steve Zeitlin asks this question about curating based on quality of writing and content in his essay, “Case Study: Where Are the Best Stories? Where Is My Story? – Participation and Curation in a New Media Age,” where he argues in part that it’s okay to delete badly written or uninformative contributions: those authors won’t care, because they most likely put little work into their submissions.[1] How does this change with inappropriate Twitter replies, where the issue is opinion, not quality, and where the replies’ authors most likely do care about what they’ve said? [2] Does the old internet adage of “Don’t feed the trolls” apply outside of forums and social media to academic/historical projects?

Since these questions don’t have easy answers, the only other part of this project that I would question is its goals: while it was certainly effective at raising awareness, I wonder if this Twitter account could have larger goals. How could this account benefit immigrants, refugees, and the current situation beyond alerting Twitter users to the issue itself? In an interview with The Atlantic, Neiss and Rabbi Schwartz mention a few other goals they have for their audience, namely for more people to visit USHMM, to donate to organizations that assist immigrants and refugees, and to continue speaking about the Holocaust the rest of the year. [3] Is there a way to do this without detracting from the tragically methodical format of the tweets?

All in all, while this project did not provide a lot of contextual information about the St. Louis, it succeeded in drawing attention to this event and its warning signals regarding current political events.

[1] Zeitlin, Steve. “Case Study: Where Are the Best Stories? Where Is My Story? – Participation and Curation in a New Media Age.” Letting Go?: Sharing Historical Authority in a User-generated World. Ed. Bill Adair, Benjamin Filene, and Laura Koloski. Philadelphia, PA: Pew Center for Arts & Heritage, 2011. 34-43.

[2] Required clarification that “opinion” here excludes hate speech, which never has a reason to go uncensored.

[3] Norwood, Candace. “A Twitter Tribute to Holocaust Victims.” The Atlantic 27 January, 2017. https://www.theatlantic.com/politics/archive/2017/01/jewish-refugees-in-the-us/514742/