About the Project
To maximize transparency and accountability, this section outlines the values underlying the project, the methodologies used, and an author positionality statement. Select the option of interest below.
Project Values
I aim to espouse the following values outlined in Milena Radzikowska, Jennifer Roberts-Smith, Xinyue Zhou, and Stan Ruecker’s article, A Speculative Feminist Approach to Design Project Management while conducting this project. I will:
Challenge existing methods, beliefs, systems, and processes; Focus on an actionable ideal future; Look for what has been made invisible or under represented; Consider the micro, meso, and macro; Privilege transparency and accountability; and Expect and welcome being subjected to rigorous critique.
As such, I commit to:
- Critically examine and challenge not only existing archival practices, standards, and vocabularies, but also my own personal beliefs, biases, and privilege.
- Document the path I have take for consideration by other individuals and institutions to conduct this work in a transparent way that holds these entities accountable.
- Foreground narratives and perspectives that have been marginalized by a legacy of classism and white supremacy in the archives field.
- Consider the micro, meso, and macro in the following ways:
- Micro-By recognizing the impact of my existence as a first-generation college student, middle-class, white, non-binary, person from the American south on my work.
- Meso- By understanding that my work will be uniquely informed by the structures, culture, departmental history, descriptive terms used, and the holdings that do and do not exist at my institution.
- Macro-By recognizing the impact of historical archival and library science practices in the broader field of memory work, including systemic erasure and exclusion of contributions by people of color in current archival work.
- Document thought processes, actions taken, and scholarship consulted to increase transparency, and personal accountability.
- Embrace, incorporate, and respond to critiques of this project in a way that emphasizes intellectual humility.
Do you have questions, comments, concerns, or critiques? Please contact me at elizabeth.danyel.james@gmail.com.
Methodology
To create a project that is transparent and holds me personally accountable, I will share an explicit statement regarding the the data I am using and the tools and approaches I am using for data analysis and visualization.
Data Collection
The data used for this project is metadata describing approximately 8,200 digitized items, primarily photographs, from manuscript collections in Marshall University Archives and Special Collections, located in Huntington, West Virginia. There is no formal collecting policy in place and acquisitions were previously based upon chance donations or the personal interests of the department head. These interests, which primarily emphasized genealogical and local history topics featuring middle to upper-class white individuals, resulted in a collection in which people of diverse backgrounds are underrepresented.
Items were selected for digitization according to no extant rationale beyond the unsystematic interests of a staff member with no archives or library science training who began the department’s digitization program. No specific standards or vocabularies were used beyond the format vocabularies built in to PastPerfect Museum Software, the prior content management system for the archive. Usage of PastPerfect metadata fields was inconsistent with no standardized local practice. This project is being conducted using the original PastPerfect metadata after a migration of the metadata from PastPerfect to Digital Commons. During this migration, PastPerfect metadata fields were crosswalked to an adaptation of the custom Qualified Dublin Core metadata fields used by Digital Virginias when harvesting metadata for ingest into the Digital Public Library of America to ensure broader interoperability in the future.
Data Analysis and Visualization
Data analysis will be conducted using the Topic Modeling Tool (mixed with Python and the Little Mallet Wrapper library) and AntConc. Data visualization tools will also include Tableau. All techniques used to synthesize data will be explicitly stated and any subsetting of data will be expressed clearly.
About the Author
Elizabeth James is an archivist and digital preservation librarian who is dedicated to critically examining practices and standards within the field of archives. Elizabeth strives to acknowledge the power and influence historically held by archives within the fields of memory work and scholarship and encourage accountability by institutions and individuals within the broader archives field.
Elizabeth is a white, non-binary, first-generation student, and archives professional who is from the American south.