Addressing the Challenge: Cataloguing Electronic Books in Academic Libraries
Bibliographic record
Abstract
This paper explores the various issues and challenges arising from e-book cataloguing experienced at the University of Windsor’s Leddy Library and the Ontario Council of University Libraries (OCUL). This discussion uses an evidence -based approach to identify and resolve issues relevant to academic libraries as well as to consortia. With the ever rising popularity of e-books within academic libraries, cataloguing librarians are actively seeking more effective methods of managing this new electronic medium, including the development of new cataloguing policies and procedures. This paper will explore the various issues and challenges surrounding e-book cataloguing and processing within academic libraries, and will identify new policies and procedures that may be used to effectively assist in e-book management.
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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 | 0.133 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.050 | 0.054 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".