D&T Extra Credit Opportunity: Probabilistically assign gender and race in your bibliography

This University of Maryland Libraries’ Research Guide on Citation Justice links to a Github repository of Python code to “probabilistically assign gender and race proportions of first/last authors pairs in bibliography entries.” Completing all the steps below can make up for 1 unexcused absence in D&T class (2023).

  1. Carefully read the repository README (all the explanatory text on the repository’s home page).
  2. Navigate to the Instructions section and follow the 3 instructions (obtain a .bib file, open the GESIS.org coding environment, and open the notebook cleanBib.ipynb.)
    • Your .bib file must match the bibliography in your final article.
  3. In the GESIS.org environment, you’ll need to upload your .bib file and run the code.
    • The code involves 5 steps.
  4. Screen-record a video that takes your audience through this process: your understanding of the README, going through the 3 steps listed in GitHub, then going through the 5 steps in cleanBib.ipynb on GESIS.org. Please also discuss your thoughts on the results.
    • Don’t record a video until you’ve successfully completed all steps. In other words, you’ll need a programmatically complete analysis with meaningful results!
    • Upload the video along with your results (e.g. in a DOC, PDF, etc.), share these items with me, and send me the link.
  5. Once I receive the link and ascertain the completion of all the steps above, I’ll drop 1 unexcused absence from your grade.

DUE DATE: December 24 at 11:59pm