Aspirational but Realistic: Reimagining Accessioning Workflows in a Medical Archives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The Medical Center Archives of NewYork-Presbyterian/Weill Cornell Medicine (MCA) was established in 1972 as the repository for what is now NewYork-Presbyterian Hospital and Cornell University's medical college. Since its inception, MCA has stewarded the records and personal papers of a large medical center through the labor of a small staff and limited resources, leading to accessioning policies and procedures that merited modernization and clarity. MCA staff have spent the last several years evaluating accessioning practices throughout the archival lifecycle, identifying key areas for improvement, and implementing clear workflows and documentation to improve efficiency and establish transparency. This case study discusses tools, resources, and methodologies utilized at MCA to implement accessioning best practices, scaled to the realities of our repository. Through incremental yet strategic changes, MCA clarified staff roles and labor, refined collection development practices, and created new internal workflows that incorporate born-digital and digitized material. A new accessioning manual serves as a tool for ongoing evaluation of our evolving practices and challenges within our medical archives context, particularly when serving as ethical stewards of historical medical records while still aiming for an access-driven approach. Strategies and methodologies are described to promote emulation in other repositories with a smaller staff or limited resources.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it