Quantifying the Science-Humanities Gap

Project Summary

Spenser Easterbrook, a Philosophy and Math double major, joined Biology majors Aharon Walker and Nicholas Branson in a ten-week exploration of the connections between journal publications from the humanities and the sciences. They were guided by Rick Gawne and Jameson Clarke, graduate students from Philosophy and Biology.

Themes and Categories
Interdisciplinary Inquiry
Data+, Digital Humanities
Year
2015
Contact
Paul Bendich
mathematics
bendich@math.duke.edu

Project Results

The team painstakingly created citation networks for several major philosophy journals and then analyzed the network structure using Gephi. Among other analyses, they found that a small fraction of the philosophy papers in this network were responsible for most of the citations to science journals. They presented their findings to the Moral Attitudes and Decision-Making Lab, and the dataset they created will be used next fall in an undergraduate philosophy class, funded under iiD's Data Expeditions program.

Aharon presenting at the Moral Atittudes and Decision-Making Lab

Download the executive summary (PPTX).

Disciplines Involved

  • Philosophy
  • Network Analysis

Project Team

Undergraduates: Nicholas Branson, Aharon Walker, and Spenser Easterbrook

Project Mentors:

 

Related People

Related Projects

Visualizing Twitter: Public Discourse and the 2018 Florida Midterm Election Recounts

This two-week teaching module in an introductory-level undergraduate course invites students to explore the power of Twitter in shaping public discourse. The project supplements the close-reading methods that are central to the humanities with large-scale social media analysis. This exercise challenges students to consider how applying visualization techniques to a dataset too vast for manual apprehension might enable them to identify for granular inspection smaller subsets of data and individual tweets—as well as to determine what factors do not lend themselves to close-reading at all. Employing an original dataset of almost one million tweets focused on the contested 2018 Florida midterm elections, students develop skills in using visualization software, generating research questions, and creating novel visualizations to answer those questions. They then evaluate and compare the affordances of large-scale data analytics with investigation of individual tweets, and draw on their findings to debate the role of social media in shaping public conversations surrounding major national events. This project was developed as a collaboration among the English Department (Emma Davenport and Astrid Giugni), Math Department (Hubert Bray), Duke University Library (Eric Monson), and Trinity Technology Services (Brian Norberg).

Queens of Antiquity

Understanding how to generate, analyze, and work with datasets in the humanities is often a difficult task without learning how to code or program. In humanities centered courses, we often privilege close reading or qualitative analysis over other methods of knowing, but by learning some new quantitative techniques we better prepare the students to tackle new forms of reading. This class will work with the data from the HathiTrust to develop ideas for thinking about how large groups and different discourse communities thought of queens of antiquity like Cleopatra and Dido.

Please refer to https://sites.duke.edu/queensofantiquity/ for more information.

Getting Granular on Social Determinants of Health

Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.

Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim

Project Manager: Austin Talbot

See all Research