Bibliographic record
Abstract
Metadating, in the field of librarianship, is the dimension assumed by cataloging in the \ndigital age; like cataloging, it refers to the process of recording metadata, that is, data \nfunctional to the identification and discovery of a bibliographic resource. It has been a \nreality for at least fifteen years in the international library context, especially in Northern \nEurope, the United States, Canada and Australia. With metadata, all relevant data \ncontinues to be recorded in an open environment – preferably according to cataloging \nrules suitable for metadata. What changes is the presentation, i.e., the way the created \ndata is returned to the reader. Italian librarians and professors of librarianship answer 10 \nquestions, offering, for the first time in an Italian journal, an overview of the topic of great \ninterest in the international librarianship scene.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.145 | 0.132 |
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".