Bibliographic record
Abstract
The Library of Congress, the National Library of Canada, and the British Library began discussing the harmonization of their respective MARC formats in 1994. The differences between USMARC and CAN/MARC were primarily in details rather than general specifications. Changes were made to CAN/MARC that eliminated many of the differences between CAN/MARC and the other two formats (USMARC and UKMARC). In addition, changes in USMARC that aligned USMARC and, CAN/MARC were approved in 1997. The nature of the differences between UKMARC and CAN/MARC has necessitated a different process of harmonization. The differences between these two formats are many in extent, details, and approach to some requirements. Although total harmonization of USMARC-CAN/MARC with UKMARC is not feasible at this time, the British Library’s program to add USMARC-CAN/MARC fields to UKMARC has increased the congruency of these formats. The National Library of Canada and the Library of Congress have begun to work on joint maintenance procedures and plan to have joint documentation.
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 | 0.043 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".