Metadata specialists in transition: from MARC cataloging to linked data and BIBFRAME (data deluge column)
Bibliographic record
Abstract
Purpose The Association of Library Collections and Technical Services, better known as ALCTS, is a division of the American Library Association. Design/methodology/approach Approximately once a month, ALCTS hosts an “eForum”, which is a moderated email-based discussion. The February 2016 ALCTS eForum was called “Career Progression in Cataloging and Metadata”. Findings It was led by Lisa Robinson of Michigan State University and Stacie Traill of the University of Minnesota. Lisa and Stacie have provided a summary of the discussion on a publicly accessible website which is referenced at the end of the column. Originality/value There were a number of comments and discussion threads which reflect the changing nature of library data or metadata; how it is created and managed; and the specific skill sets of catalogers and metadata librarians. This installment of the Data Deluge contains an examination and discussion of challenges associated with the role and career progression of catalogers and metadata specialists as they establish their place in the emerging linked data movement in libraries.
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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.017 |
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".