Wikipedia Pages for Underrepresented Archivists: Creating Representation through an SAA Foundation Grant-Funded Documentation Project
Bibliographic record
Abstract
ABSTRACT In spring 2019, university archivist April Anderson-Zorn and special formats cataloger Eric Willey, both at Illinois State University (ISU), submitted a grant request to the Society of American Archivists (SAA) Foundation. The team requested funds to hire an ISU graduate student to create content for Wikipedia pages for underrepresented archivists. The grant aimed to fill a content gap on the site by including the biographies of female, Black, LGBTQ+, and other underrepresented archivists to highlight their accomplishments in the profession. With the help of graduate student Stephanie Collier, the project surpassed its original goal of fifteen pages, with over forty pages now on Wikipedia. Though Collier was successful in her efforts, she experienced setbacks throughout the process, including biases and harassment from some Wikipedia editors. This article reviews the literature on the history of Wikipedia and bias found within the Wikipedia community, provides an overview of the project, discusses privacy concerns in creating Wikipedia pages, and suggests the next steps for continuing the work to bring representation for underrepresented archivists to the Wikipedia platform. The article also examines Collier's experience working to bring representation to archivists while completing a graduate degree in history in the early months of the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".