Shedding light on an important archive: digitizing the Winnifred Eaton Reeve fonds
Bibliographic record
Abstract
The Winnifred Eaton Reeve fonds, a significant collection in the literary archives at the University of Calgary, chronicles the works of Winnifred Eaton Reeve (1875-1954), a pivotal early Chinese North American fiction-writer who assumed the Japanese persona “Onoto Watanna.” This collection became the focal point of a comprehensive digitization project driven by substantial demand from academic researchers. This project was unique as it encompassed digitizing the entire collection. The session will include a brief overview of our first venture in digitizing an entire archival collection during a time of change. Attendees will learn about our approach to this digitization project and its multifaceted aspects that needed to be considered. The inherent fragility of the artifacts mandated extreme caution during every interaction, underscoring the need for digitization to safeguard and enhance accessibility for present and future Winnifred Eaton Reeve enthusiasts. Significant challenges and opportunities encountered during the project will be highlighted, including metadata mapping across disciplines, managing sensitive content, and adapting to unforeseen changes in the Canadian copyright landscape as well as substantially updating the finding aid and creating an online guide to increase access to the collection. With the successful completion of the digitization project, access collection either the physical or digital can be an effective way to raise awareness of this important Canadian author and generate new understandings through researching Winnifred Eaton Reeve’s life and works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.033 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".