Mapping MARC 21 Linking Entry Fields to FRBR and Tillett’s Taxonomy of Bibliographic Relationships
Bibliographic record
Abstract
Bibliographic relationships have taken on even greater importance in the context of ongoing efforts to integrate concepts from the Functional Requirements for Bibliographic Records (FRBR) into cataloging codes and database structures. In MARC 21, the linking entry fields are a major mechanism for expressing relationships between bibliographic records. Taxonomies of bibliographic relationships have been proposed by Tillett, with an extension by Smiraglia, and in FRBR itself. The present exercise is to provide a detailed bidirectional mapping of the MARC 21 linking fields to these two schemes. The correspondence of the Tillett taxonomic divisions to the MARC categorization of the linking fields as chronological, horizontal, or vertical is examined as well. Application of the findings to MARC format development and system functionality is discussed.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".