Library data: what is it and what changes do libraries need to make? (the Data Deluge Column)
Bibliographic record
Abstract
Purpose In 2016, the “Data Deluge Column” explored the sometimes frustrating reality of cataloguing and metadata librarians as their discipline underwent change. Design/methodology/approach The column, called “Metadata specialists in transition: from MARC cataloguing to linked data and BIBFRAME”, alluded to the ongoing and significant changes in the practice of cataloguing and metadata creation, but did not delve into the nature of the changes and what they mean for libraries in general. Findings This instalment of the “Data Deluge Column” expands that discussion by exploring the emerging model for the data that libraries create and manage. Originality/value It seems that it has taken about 20 years to overcome the inertia required to begin to reinvent the practice of and environment for creating library data. Perhaps, some of this inertia is because of predictions of the current distress and pressure felt by cataloguing departments today.
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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 | 0.041 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.057 | 0.046 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".