Using Jefferson’s Letters to Recreate Networks and Narratives

In her article “The Image of Absence,” Lauren F. Klein advocates for the use of data visualizations in recreating historical narratives. Specifically, much of her paper is spent analyzing Thomas Jefferson’s letter collection as catalogued in “The Papers of Thomas Jefferson” digital database. Her primary subject is a man named James Hemings, a slave of Jefferson’s who eventually became a chef at his French estate. Throughout her article, Klein notes that while Jefferson’s letter collection is extensive, researchers are rather limited in the potential claims they can make regarding the Antebellum period due to Jefferson’s narrow point of view. Furthermore, Klein suggests that data visualization methods and specific searches within the collection can elicit more telling information of the time period.

Klein notes that one of the main issues with any written work of this time revolves around the fact that they were published by white, landowning males. Therefore, even works written by slaves don’t yield an accurate portrayal of the networks and social structures that truly existed during Jefferson’s lifetime. Klein urges that in order to reconstruct these narratives, “We must look to the pathways of connection between persons and among groups, the networks of communication in which these men and women engaged, and the distributed impact of the labor they performed” (665). Here, Klein asserts, is where data visualizations can fill the gaps that individual letters and correspondences create. Perhaps the most interesting example of the role of data visualizations in her article, Figure 3 depicts the “networks of relations” embedded in a specific set of letters pertaining to James Hemings’ life. The figure, pictured below, was created by a computing process that identified groupings of words that resemble people’s names in 58 letters referencing Hemings. Klein concludes that figure exhibits the intricate relationships that existed between all members of Jefferson’s network, including his family, political correspondents, friends, plantation staff and his slaves. Examining Hemings’ node on the figure, it becomes clear that unlike the traditional narrative of the time, he was in correspondence with a variety of people, including Jefferson, Jefferson’s friends and even free plantation staff. Additionally, Klein notes that the significant amount of correspondence between Jefferson and the Hemings family suggests that he truly relied on his slaves and actively sought to communicate with them. The overall importance of the figure is to highlight the complex networks and interactions that are often difficult to see in such a one-sided historical narrative as the Antebellum period.

 

Visualization of the network of relations within the "Hemings  Papers." Width of arc indicates relative frequency of correspondence. Image by Klein
Visualization of the network of relations within the “Hemings Papers.” Width of arc indicates relative frequency of correspondence. Image by Klein

Digital Humanities and a New Definition of Knowing

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Klein proposes the viewpoint that digital humanities requires one to sit back and analyze the extent to which we are knowledgeable about certain information. A deeper analysis of Jefferson’s slave James Emmings whose true identity is no fully known leads her to issue a call to action for digital humanity practitioners to go out into the world and uncover the information that has yet to be found. She believes that no longer can we be satisfied with knowing, but now we must also ponder how we know things to be true, their origins.

While Klein does bring up a very good point, I believe that one must question the completeness of her claims. Why is now the time for digital humanities to theorize their claims? Yes we have more technology that can aid in deeper research of how we know information, but if this practice was as necessary as she deems it to be, would it not have begun earlier? We are able to find out trivial things, such as Hemmings being educated, but this does not do much to help mankind as a whole in the future.

I would agree to the statement that digital humanities will reveal much information, but I cannot help but wonder how that information will be put to use. If we as of now do not fully “know” certain subjects well, when will we get to that point as a collective society? Is it worth it to swim in unclear waters in search of how we know information if we do not know what fully “knowing” consists of? It seems somewhat pointless that Klein would state our inability to fully have understanding of a certain subject, without defining full understanding. Eventually a limit on how much we know will be set, but until that time, the concrete purpose and usefulness of digital humanities will remain incomplete.

Klein, Laura.”The Image of Absence: Archival Silence, Data Visualization, and James Hemings.” Duke University Press, 2013.Print

Beginning to See

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James Hemings was lost for the past 200 years. James represents the many who were easily left out of textbooks. As a slave and cook to Thomas Jefferson, the third president of the United States, James had been forgotten. Even in “The Papers of Thomas Jefferson” database which has over 25,000 entries, one could perform a search for James Heming and find no results. What allowed us to find James is advanced computational linguistics and data visualization. Through these techniques we can begin to peel back the mystery of James Heming and to discover him through the writings of Thomas Jefferson. These techniques allows historians to build profiles on indiviuals who left little to no evidence of their existence. Like dark matter we now know these individuals existed, but all we really have is a ghost of their existence.

What I find most interesting about these techniques is the fact that you can build a profile about an individual based on someone else’s paper trail. I find this intriguing because our profiles are created by our paper trails, but per say what if one never used the internet or left a paper trail. could we use say our parents, our siblings, our best bestfriends data and paper trails to create profiles about these modern day ghosts? Lauren Klein’s piece truly shows the power of data technology and how transparent our identities are in this modern world.

Week 3 Observations

Tuesday, January 27:

  • 4 hats in class
  • 1 pair of crutches
  • 1 person wearing flip flops (high temperature: 46 degrees)
  • 3 people wearing glasses, unknown number wearing contacts
  • still only 3 girls in class
  • 9 shirts with collars
  • ~24 minutes spent building panopticon

Thursday, January 29:

  • 2 patagonia fleeces
  • 6 people wearing glasses, presumably lower number of people wearing contacts
  • 10 drinks on tables
  • Time of Arrivals:
  • Time of Arrival

The Atlantic – ‘The Cloud’ and Other Dangerous Metaphors

Interesting read from The Atlantic:

Underlying the discussion has been a tangle of big, thorny questions: What policies should govern the use of online data collection, use, and manipulation by companies? Do massive online platforms like Google and Facebook, who now hold unprecedented quantities of sensitive behavioral data about people and groups, have the right to research and experiment on their users? And, if so, how and to what extent should they be permitted to do so?

…

Data escapes attempts to fit it neatly into a single conceptual box. Consider three phrases—now so commonplace as to be unremarkable—that we use to talk about data:

  • “Data Stream,” which refers to the delivery of many chunks of data over time;
  • “Data Mining,” which refers to what we do to get insightful information from data; and
  • “The Cloud,” which refers to a place where we store data.

These tropes are notable because they use distinct, physical metaphors to try to make sense of data within a specific context. What’s more, all three impute radically different physical properties to data. Depending on the situation, data is either like a liquid (data streams), a solid (data mining), or a gas (the cloud). Why and how these metaphors get used when they do is not immediately obvious. There are tons of alternatives: Data could be stored in a “data mountain,” or data could be made useful through a process of “data desalination.”

…

And in all our talk about streams and exhaust and mines and clouds, one thing is striking: People are nowhere to be found. These metaphors overwhelmingly draw from the natural world and the processes we use to draw resources from it; because of this, they naturalize and depersonalize data and its collection. Our current data metaphors do us a disservice by masking the human behaviors, relationships, and communications that make up all that data we’re streaming and mining. They make it easy to get lost in the quantity of the data without remembering how personal so much of it is. And if people forget that, it’s easy to understand how large-scale ethical breaches happen; the metaphors help us to lose track of what we’re really talking about.

X-Post: Economic Adaptations of Big Data

Wanted to share a post that I wrote for my Digital Anthropology class at Davidson that discusses how business are attempting to shift to Big Data cultures:

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“Big Data”

epitomizes a buzz word. Tech blogs began following the emerging field of analyzing very, very, very large amounts of data that humanity is generating at an unprecedented pace about 10 years ago. What was once an interesting exercise has become much more as startups have built tools that allow businesses to gather and analyze big data in real time. Tools like Hadoop and MongoDB are few among many of the entrants vying to lead the big data revolution that transcends mainstay relational SQL databases in favor of NoSQL databases that hoard every last datapoint that they can, even if those data provide no perceivable analytical value at present.

While many organizations are waking up to the potential of big data, most are slow to understand it. Companies are quick to advertise that they “leverage big data analytics” to “provide value” and discover “new synergies”, but more often than not they are putting a new dress on the same dog. While tools to harness big data are developing rapidly, people can be the limiting factor in catalyzing a shift to big data culture. This was recently espoused by Mark Gazit in an op-ed on TechCrunch. He cites research that catalogues the talent drought for data scientists, noting that there simply isn’t enough human horsepower available for companies attempting to integrate big data into their core business models. Rather than calling for more training programs, Gazit champions the efficacy of machine learning and artificial intelligence in bringing big data to the masses:

Ultimately, by solving the issues that prevent full optimization of big data analytics — especially the human factor and its disproportionate impact on the current-day process — organizations will be able to detect and address all types of threats and opportunities much more rapidly. This capability is becoming increasingly crucial in an era when data is being generated by both humans and machines, and is sure to become a pivotal way for businesses to create situational awareness, detect issues and optimize operations to achieve their business objectives. – Mark Gazit, CEO of ThetaRay

It remains to be seen how machines and humans will complement and subordinate one another as the data economy continues to evolve. Greater efficiencies, lower costs, and more predictability are inevitable – the question is, who will we have to thank?

Note: I am sharing this post out of interest and relevancy, not for any form of academic credit. This post was created as an assignment for a separate class. 

Group 5 Data Collection Week 1/26

Tuesday:

  • 13 laptops out, 10 of which were open
  • 9 of these were Apple
  • 2 tablets out: 1 apple, 1 windows
  • 15 people had paper notebooks
  • 2 had both a laptop and paper notebook
  • 1 person had nothing out at all

Thursday:

  • 19 laptops out, all open
  • 14 of these were Apple
  • 3 tablets out: 2 apple, 1 windows
  • 11 people had paper notebooks
  • 5 people had both a laptop and paper notebook
  • 0 people had nothing out

Data Collection

Tuesday (1/27)

10 pairs of blue jeans

3 hats

5 sunglasses

2 crutches

2 coffee cups

2 fraternity shirts

2 econ pens

1 Ticonderoga pencil

13 computers being used

-6 apple computers

-1 tablet

7 green markings on board

4 blue markings on board

18 questions asked by Dr. Sample

7 Nike shoes

 

Thursday (1/29)

25 questions asked my Dr. Sample

18 Iphones

8 androids

4 other phones

5 drinks on tables

26 computers being used

1 hat

4 Davidson shirts

1 fraternity shirt

Hodgepodge of Observations from 1/27 and 1/29

Tuesday (1/27):

  • Words written on the board by Dr. Sample: Dataveillance, surveillance, and superpanopticon (this word was underlined)
  • 18 comments written on the board by students, responding to Foucault’s statement, “Visibility is a trap”
  • First interactive class exercise and first time Dr. Sample has recorded us
  • 3 people wearing hats to class
  • Class finished at 2:52PM

Thursday (1/29)

  • Words written on the board by Dr. Sample: Inscribe, writing (written underneath inscribe), represent, and depict (written underneath represent)
  • No comments written on board by students
  • First YouTube video shown in class
  • In preparation for the quantified self assignment, it looks like there are 18 IOS devices, 8 Androids, and 4 Others
  • Ended class on the idea that the act of observation changes the object being observed

General observation from both days:

With some exceptions, it seems as though the class organizes its seating arrangement by gender (all 3 girls at one table), fraternity, athletic organization, and college year.

Distribution of Mac and Windows Users in DIG 210

DIG 210- Data Collection: Distribution of Technology
Tuesday:
5 Windows Computers
7 Mac Computers
1 Windows Tablet
2 Mac Tablets

Thursday:
(Statistics Obtained Prior to Use of Lab Computers)
5 Windows Computers
1 Windows Tablet
2 Mac Tablets
12 Mac Computers

In addition to this, as we learned in class, we have 18 iPhone users (iPhone 5 or later), 8 Android users, and 4 people who use a different type of mobile device.