The Outcome of the ArtFrame Project: A Domain-Specific BIBFRAME Exploration
Bibliographic record
Abstract
The ArtFrame Project, a part of the Linked Data for Production (LD4P) collaboration, was a domain-specific, linked-open-data (LOD) initiative that explored the metadata practices of art libraries and museums. The project, headed by Columbia University Libraries and including major art institutions and the Cataloging Advisory Committee (CAC) of the Art Libraries Society of North America (ARLIS/NA), focused on developing an extension to the Bibliographic Framework Initiative (BIBFRAME) tailored to the needs of art catalogers. This article describes the history of the project and its collaboration with the LD4P Rare Materials Extension Group to produce a shared ontology, the Art & Rare Materials BIBFRAME Ontology Extension (ARM).[This article is an expansion of a presentation at the ARLIS/NA conference held in New York, New York, in February 2018.]
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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 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".