Bibframe advances in 2016: Perspectives of the new bibliographic model
Bibliographic record
Abstract
The progress of the Bibframe Initiative in 2016 and the first quarter of 2017 is discussed. The difficulty of transforming the MARC 21 format into a new format lies within the model itself, but also in building a new cataloging ecosystem, both for catalogers and for library software. In spite of this, some of the most important libraries in the world are determined to follow this path. An analogy is drawn with the theoretical model and the subsequent cataloging rules promoted by IFLA, paying attention to the transition from AACR2 and ISBD to RDA, and from the FRBR family to LRM. The advantages that derive from the adoption of a data model adapted to RDA and linked open data are emphasized. OCLC, participating in the Bibframe initiative, has also explored the application of linked open data based on Schema.org. It is reported that the new version of the Bibframe 2.0 vocabulary has already been approved. And, finally, in 2107, the publication of new specifications to transform MARC 21 records to Bibframe will serve as an extension of the knowledge of this new standard.
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 | 0.038 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.019 |
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".