Conclusion
Given the nature of the writing style Hamilton had along with the goals of our project, it is really difficult to narrow down the markup to be more precise. We would have preferred to have more precise markup that contained more tagging and the markup itself to be more precise.
This was a difficult task since Hamilton would take up to entire paragraphs to make a point about a key issue / opinion. So, the nature of the writing is difficult to tag.
If we were to do it again, we would spend more time going over individual markup to make sure it was more precise and more fruitful. Meaning, it contained a wider variety of elements. Given that three individuals completed the markup, variation and levels of error are bound to occur. In addition, this implies that there are three different sources anf thus different opinions on what certain phrases and statements mean.
What did the data tell us?
Hamilton was a known elitist snob who thought the opposite on many key political views. Namely, he envisaged a strong executive and legislative branch, with the ability to tax and wage war on other countries. Given these political beliefs, it was surprising to see results that Hamilton did possess and hold views that the public supported.
The data also supported some of these ideas. It showed that his notable opposition to a bill of rights truly consisted of elitist rhetoric. It also supported the inverse. Specifically, because this elitist rhetoric supported this belief held by Hamilton, personal rights such as a free trial by jury showed strong populist views. The topics of the judiciary contained words such as: equity, juries, and proper.
These words are emblematic of fairness and justice, which is what the revolting colonists believed the British Crown lacked.
Final Remarks
We certainly came into some difficulties when designing and completing this project. Due to time constraints, we could not use all of the digital humanities technologies that we desired to use, namely network analysis. We believed that that would generate data and images that come closer to narrowing connections between certain phrases to topics and themes.
We (Nate, Patrick, and Shane), want to personally thank our project mentor, Evan Ressel, and our course instructor, Dr. David Birnbaum for all their assistance and time in making this project become a reality.
We hope you enjoyed!