The FRBR Family of Conceptual Models: Toward a Linked Bibliographic Future
Bibliographic record
Abstract
Foreword 1. Introduction: Be Careful What You Wish For: FRBR, Some Lacunae, A Review 2. The VTLS Implementation of FRBR 3. FRBR: The MAB2 Perspective 4. Implementing FRBR to Improve Retrieval of In-House Information in a Medium-Sized International Institute 5. A Strange Model Named FRBRoo 6. Item, document, carrier: An Object Oriented Approach 7. Modeling Aggregates in FRBR 8. Arrangement of FRBR Entities in Colon Classification Call Numbers 9. FRSAD and the ontology of subjects of works 10. FRBR Entities: Identity and Identification 11. FRBR/FRAD and Eva Verona's Cataloging Code: Toward the Future Development of the Croatian Cataloging Code 12. Evaluation of RDA as an implementation of FRBR and FRAD 13. Conceptualizations of the cataloging object: A critique on current perceptions on FRBR Group 1 entities 14. From the FRBR Model to the Italian Cataloging Code (and Vice Versa?) 15. The Contribution of FRBR to the Identification of Bibliographical Relationships: The New RDA-based Ways of Representing the Relationships in Catalogs 16. Analysis of Work-to-Work bibliographic relationships through FRBR: A Canadian Perspective 17. Composing in Real Time: Jazz Performances as in the FRBR Model 18. Identifying Works for Japanese Classics for Construction of FRBRized OPACs 19. FRBRizing Bibliographic Records Focusing on Identifiers and Role Indicators in the Korean Cataloging Environment 20. What do Users Tell us About FRBR-Based Catalogs? 21. Representing the FR Family in the Semantic Web 22. YouTube: Applying FRBR and Exploring the Multiple Description Coding Compression Model 23. FRBR and Linked Data: Connecting FRBR and Linked Data
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.019 | 0.040 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".