Digitizing the Winnifred Eaton Reeve Fonds Project: Sometimes it Takes a Village
Bibliographic record
Abstract
The University of Calgary’s Archives and Special Collections houses the Winnifred Eaton Reeve archive, a popular collection for literary researchers investigating Winnifred Eaton Reeve, a pivotal early Chinese North American fiction-writer who assumed the Japanese persona “Onoto Watanna.” Winnifred Eaton Reeve (1875-1954) was a successful novelist in North America as well as a Hollywood editor, story and screenplay writer. This collection stands as the second most frequently consulted fonds in our literary archives. 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. The rising demand for digital access from external academic researchers became the catalyst for a full-scale digitization project. This project was unique not only as it encompassed digitizing the entire collection but because a cross-departmental team was created to handle the project. The presenters will discuss how the complexities of a team approach for the digitization project – from retrieval, description, and handling of fragile materials to addressing metadata mapping, sensitive content, and navigating changes in the Canadian copyright act, substantially updating the finding aid and creating an online research guide drew upon a wide-range of information experts from across the library system. Insights from our first endeavor in digitizing an entire archival collection will be shared, offering lessons learned and future directions.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".