Exploring Demographic Homogeneity in Librarianship

To see higher quality versions of these visualizations, check out the project write up (linked below)

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Network visualization of subject terms from “In the Library with the Lead Pipe” (2013-2024) where topics related to labor and equity are highlighted.
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Map of employment quotients for library jobs relative to accredited library schools in the U.S. for 2023.  There is a low concentration of librarians in the South West, which does not correspond to the distribution of accredited schools.  Data Source: U.S Bureau of Labor Statistics, Occupational Employment and Wage Statistics, and the American Library Association.
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Line chart of the average salary for librarians compared to other professions, 2017-2023.  Similarly paid professions include Aircraft Mechanics and Service Technicians and Crane and Tower Operators. Data Source: U.S Bureau of Labor Statistics.

For this project, I modified and analyzed a series of data visualizations which I created across a semester of work, related to professional diversity within librarianship as a field. The project sought to explore the research question, “What is the demographic breakdown of librarianship as a field, and what potential underlying causes may contribute to the overrepresentation of white women in librarianship (relative to the demographic breakdown of the population of the United States?”, using a collection of graphs, maps, and network visualizations.

For the project I used several data sources, and practiced both cleaning of an existing dataset, and creation of a new dataset from an information source. For example, I created several charts using data from the Bureau of Labor Statistics, about the demographic breakdown, and payscale of librarianship compared to other professions. I also built a dataset of ALA accredited library programs using data from the ALA, which I used to create a map of career outcomes relative to program locations. Finally, I also built a dataset of subject terms from In the Library with the Lead Pipe articles from 2013-2024, which I used to conduct subject analysis in a series of network visualizations, to explore the frequency with which articles mentioned topics related to equity and labor from 2013-202. For this project, I utilized the data visualization tools Tableau and Gephi, and utilized Open Refine, Google Sheets, and R scripts to analyze, organize and clean the 3 datasets I used for the project.

For this project, I independently gathered the data I used, conducted user testing on the charts I created, designed and produced the data visualizations, and wrote the final report I produced about the project.

OpenRefine, R, Gephi, Tableau, Research