Numismatics & Bibliographic Description: How Rutgers University Libraries Described Coins with MODS
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
Realia pose challenges when utilizing bibliographic metadata standards. Rutgers University Libraries, in collaboration with Rutgers University’s Classics Department, created a large digital library collection of ancient Roman coins in RUcore, Rutgers University’s Community Repository. RUcore records use Metadata Object Description Standard (MODS) for descriptive metadata and many custom fields. Therefore, it was necessary to adapt numismatic description to fit this structure. During the planning stage of the project, Numismatic Description Standard (NUDS), a numismatic database standard implemented and maintained by the American Numismatic Society (ANS), and VRA Core, an art-centered XML metadata standard created by the Visual Resources Association, provided valuable insights. However, this project faced challenges in terms of interoperability and time constraints that required altering the team’s approach to this unique set of resources in a digital library environment. Key issues were encoding B.C.E. dates in a machine-readable format for optimal searching and browsing, developing local controlled vocabularies, providing subject access to the iconography on coins, and the research-intensive work of metadata description. This article provides “how to” information, as well as a critical analysis of lessons learned and opportunities for improvement as the linked data landscape has changed both bibliographic and numismatic description.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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