Mei Yu
Economics Major, Senior – Data Specialist
Marysville, CA
I believe this world has become increasingly technological and Digital Humanities embraces this and uses technology to answer questions. This allows me to gain an advantage that will help further my future career and bring unique skills to any company I work with.
I really enjoyed working with our data set and seeing the process in creating a Digital Humanities project. As this is my first exposure to this, I was really curious to see the steps my group would take to creating an innovative and lasting website.
Marissa Kamisugi
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Political Science Major, Global Studies Minor, Senior – Project Manager
Hawaii
My interest in Digital Humanities stems from my desire to build upon and improve my ability to problem solve. I believe Digital Humanities functions to identify questions, problems, and ideas in order to create meaningful solutions and new understandings. The intersection of data, art, and writing excites me most about the work in this field.
One of my favorite aspects of this project was watching aspects of our assignments come together to create the foundations of our website. I enjoyed being able to build each part step by step throughout the quarter. It made the project both manageable and engaging!
Theodora Ng
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Communication Major, DH Minor, Senior – Content Specialist
Hong Kong
As a Communication student, Digital Humanities allows me to expand the scope of my knowledge and skill set, enabling me to apply digital skills - website creation, data visualization, coding - to my future work.
My favorite aspect of the project was the research process, during which I was able to learn about the unique history of the Eastern State Penitentiary. It was particularly interesting not only due to the contrast between a bygone prison system and those of today but also because of the manifest influences of the Pennsylvania system on various elements of modern prisons.
Gillian Campbell
MIMG Major, DH Minor, Junior – Mapping Specialist
Woodside, CA
As someone who wants to go into the medical field, having the skills Digital Humanities teaches me will give me a new perspective on medical issues. Digital humanities has changed the way we study the history of health and medicine, allowing us to see a historical world through a screen. I want to be able to learn the skills and understand the significance of these technologies so I am able to apply them in a medical career.
I love working with others and being able to work as a team has allowed me to view our dataset in new perspectives. I also love creating the data visualizations because it’s like a puzzle where you need the right pieces to get the end product.
Natasha Nainani
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Statistics Major, DH Minor, Junior – Web Design
Hong Kong
As a Statistics major, I am constantly working with data. I have learned to clean, manipulate, and visualize data in a manner that allows me to develop strong technological and problem-solving skills. Digital Humanities interests me because it adds another lens to my degree by introducing a humanities aspect, allowing my perspectives to be more well-rounded.
This project excited me because it challenged me to work with several tools I have never worked with before, including Breve, Voyant Tools, and Palladio. I look forward to learning more practical skills in the minor in the near future.
Diana Park
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English Major, Creative Writing Concentration, Senior – Content Specialist
La Crescenta, CA
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I am interested in how DH works as an intersection of many different fields.
My favorite aspect of the project was the research. Reading about the history of the Eastern State Penitentiary and how contemporary scholars view the penitentiary prison system was a very enriching experience. In addition, trying to figure out how to shape our research into a narrative for our website was challenging but fun.
Wilson Li
Economics Major, Senior – Web Specialist
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Hong Kong
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As an aspiring finance professional, Digital Humanities incorporates many essential data analytics and visualization tools that will aid me in my career, allowing me to understand large volumes of data in a short period of time than presenting it in an effective manner.
I enjoyed working with our data set and exploring the correlation between the data and our research question. I also really enjoyed working with the website, exploring the different layouts that would best present the information we’ve gathered.
Andy Nguyen
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Economics Major, Senior – Data Visualization Specialist
San Jose, CA
My interest in digital humanities stems from my interest in business intelligence analytics. The study of digital humanities also empowers my skill set in the field of technology and startup ventures.
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I enjoyed learning how to work with CSV files. The process of cleaning data, visualizing data, and finding relationships through metadata gave me insights into the power of digital humanities technologies and its practicality in the professional world.
About the Project
SOURCES
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Sources refer to raw materials such as files, texts, images, sounds, and course research. Our team chose this particular dataset because we were intrigued by the personal information that it possessed, the way in which the records were organized, and the overall history of the penitentiary. We believed that there was potential for us to leverage information presented on the dataset to address a larger topic or issue related to the prison and the justice system. Our background research about the penitentiary and its reform system helped to guide our series of questions and additional research. We found images of the original records, a virtual tour through the prison, and articles explaining why the prison is so historic, which enhanced our understanding of our dataset.
PROCESS
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Processing involves asking questions, cleaning data using Breve and OpenRefine, as well as exploring data using RAW and Tableau. In observing and analyzing the data through these platforms, our team was able to address our initial questions and generate new ones. When deciding on which topics from our dataset we would pursue, we leveraged our understanding of descriptive metadata. According to Jenn Riley’s article “Understanding Metadata,” descriptive metadata is defined as information about the content of a resource that aids in finding or understanding it. In our case, the descriptive metadata we gathered about the penitentiary’s reform system helped us focus our research on the dataset’s information about religion and literacy.
PRESENTATION
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Presentation is a product of sourcing and processing. It requires an explanation of how sources were identified, processed, and analyzed. In our team’s case, the presentation is the design and function of our website. By referring to Nathan Yau’s book in Data Points: Visualization That Means Something, we were able to concentrate on creating visualizations that focused on our topics of interest such as religion, literacy, sentencing length, and origin. His notes about creating data graphics that effectively highlight trends and patterns inspired us to apply various colors and visuals to enhance our website. For example, in our visualization of various sentencing locations we applied different colors to make clear distinctions. We also used a mix of warm and cool colors throughout our Data Exploration page to appeal to various design preferences.
OVERVIEW
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In Scott Weingart’s blog post titled, “Question- and Data- Driven History,” he explains the process of creating a team with a mission to assemble a readable short story over the course of 12 hours. He noted that before the project began the participants established some ground rules. The main rules were that participants would need to be capped at a dozen, would need to be committed to being available every day, and would need to come with diverse skills and backgrounds. If these ground rules were to be applied to our project, I think our team succeeded in our ability to demonstrate diverse skills and backgrounds. However, meeting consistently and staying on the same page proved to be a challenge while developing our presentation. Nevertheless, we worked hard to strategize and collaborate efficiently in order to accomplish these three steps.
Data critique
Scott Ziegler and Michelle Ziogas, along with other library science students at Drexel University, generated the dataset for prisoner records from the Eastern State Penitentiary by transferring information from the original admissions book for the State Penitentiary of the Eastern District of Pennsylvania.[1]Split into seven volumes, the admissions books were written as the prisoners were admitted to the prison, and provide a first-hand account of criminal justice in the era of the 1830s. The chosen dataset encompasses the first volume.
A cursory glance at the dataset reveals each prisoner’s legal name, age, ethnicity, religion, place of birth, sentence duration, prisoner number, date of prison admission, criminal offense, number of convictions, discharge note, level of literacy, and judgment of moral character. The dataset contains information on prisoners admitted from 1830 to 1839, with a majority of the prisoners born in Philadelphia. The age range at this prison is broad, with the youngest admitted person at age 12 and the oldest person at 71. Furthermore, a majority of the prison is identified as male, with a few outlying females. The most popular crimes committed are burglary and larceny, with sentences ranging from one year to twelve years. In regards to the number of convictions, a majority of prisoners have one conviction while the prisoner with the most convictions has six.
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Though the prison records present a substantial amount of information about prisoners at the Eastern State Penitentiary, there are many aspects of the prison that cannot be disclosed from exclusively observing the dataset. It is important to note that this dataset does not give the historical context of prison recordkeeping or the development of prison systems. For instance, there is no metadata about prison conditions or about how the prisoners were affected by these conditions, and the records do not provide information on the physical layout of the cells within the penitentiary. Furthermore, the data compilation process involved interpretation and categorization of hand-written records. Compounded by the rudimentary state of prison recordkeeping at the time, the dataset is rife with missing, incomplete, and ambiguous information.[2] It is unclear whether Ziegler and Ziogas chose to leave certain information out or if it was not included in the original source, as illegible handwriting may have been causes for blanks in the dataset.
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Under the “Ethnicity/Religion/Occupation” variable, prisoners were often only racially identified when they were of color. Descriptors such as “Black,” “Light Black,” “Mulatto,” “Dark Mulatto,” and “Very light Mulatto” demonstrate that prisoners were not simply being classified as “white” or “non-white,” but rather classified by the specific shade of their skin color, suggesting an additional degree of racial discrimination within the prison. There is exclusive record of non-white ethnicity and no record of white ethnicity, except for the use of the descriptor “Irish,” another discriminated group at the time. Furthermore, some prisoners who were not categorized racially were instead categorized by their religion or occupation. The very grouping of “Ethnicity/Religion/Occupation” is problematic – not only are there numerous empty entries, but also the consolidation of three distinct variables leads to a greater dearth of information regarding these characteristics. The lack of complete documentation may hinder future efforts to study relationships between these variables and others in the dataset, as well as those among the three characteristics themselves.
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Interestingly, while the original data source notes that the dataset encompasses records of Eastern State Penitentiary prisoners numbered 20 through 1124, there are only around 500 prisoner entries.[3] It is unclear whether the set is a complete account of all prisoners within the defined parameters – it is possible that prisoners were numbered non-sequentially, in which case the record is whole; it is also possible that prisoners were numbered in sequence, which would suggest the absence of over 500 entries from the dataset. Furthermore, the nature of the data is such that it only accounts for those who were convicted of their crimes. From only the dataset, it is impossible to find information about those who committed crimes but were not convicted. If one were to presume there were factors influencing which criminals were convicted – such as how those of lower socioeconomic status may have been more likely to be incarcerated than those of higher socioeconomic status – then it would follow that the dataset is limited to the information on the subset of convicted criminals, and cannot reveal information about the larger set of all those who have committed crimes. Lastly, the “Description” variable, which details a prisoner’s moral character, is highly subjective and must be treated as potentially unreliable metadata. The identity of the book’s recorder and their role in the prison is not mentioned in the dataset, though the original data source indicates that a man named Reverend Thomas Larcombe kept the admission ledger of the chosen dataset.
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Although missing data points and biases inherent in the organization of the data limit the answers we can glean from the dataset, these limitations instead allow us to ask productive questions about the history of criminal justice in the United States. By looking at literacy and religion explicitly recorded in the dataset and exploring its significance in relation to the Eastern State Penitentiary’s mission to reform its prisoners, we can construct a deeper understanding of the factors that shaped criminal justice in Pennsylvania during the early 1800s.
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[1]Ziegler, Scott and Ziogas, Michelle, "Eastern State Penitentiary Admission Book A," - . 22. Philadelphia, PA: McNeil Center for Early American Studies [distributor], 2016. https://repository.upenn.edu
[2]Shoemaker, Robert. "The Growth of Record Keeping about Convicts." The Growth of Record Keeping about Convicts | The Digital Panopticon. Accessed October 16, 2018. https://www.digitalpanopticon.org/The_Growth_of_Record_Keeping_about_Convicts.
[3]State Penitentiary for the Eastern District of Pennsylvania Records, American Philosophical Society.