Six Impossible Things: Moving KBART into the Next Decade
Bibliographic record
Abstract
KBART is one of the most successful NISO recommendations today. Formally supported by over 80 organizations across all stakeholder groups, it enables a standardized transfer of data between content providers and knowledge bases. Most recently KBART added an automated process to transfer holdings data to localize an institution’s knowledge base holdings. While KBART was originally built to focus on journal and book data, the world has moved on—the different flavors and nuances of open access, the increased use of audiovisual material, holdings at the chapter and article levels, and issues around translations, transliterations, and author names are just some of the challenges that are disrupting the flow. So what is next for KBART? How does it adapt to continue to solve the data flow problems that libraries, publishers, and knowledge base providers face today? The presenters in this session, all members of the NISO KBART Standing Committee and/or the KBART Automation Working Group, discuss the status and future of a “Phase III” revision of NISO KBART that aims not only to clarify the existing recommendations but also to expand them to address the new challenges, including the support of additional content types beyond serials and monographs and improvements to item-level discovery and access.
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 | 0.057 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.023 | 0.039 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".