Numismatics & Bibliographic Description: How Rutgers University Libraries Described Coins with MODS
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.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".