Let Me See That eBook: Managing Cataloguing and Access through Collaboration
Bibliographic record
Abstract
Electronic resources have become a vital part of research collections. Online journals and databases are solidly planted in academic library collections and in the research habits of faculty and students. Over the past several years, academic libraries have seen this increasing demand for electronic resources expand into electronic books. Collection budgets have shifted to meet this demand through the increased acquisition of electronic books and electronic book packages. However, the sheer number of titles involved has made providing digital access to electronic books through traditional cataloguing extremely challenging. It has become clear that traditional in-house cataloguing of electronic books is neither feasible nor sustainable, even with cooperative cataloguing tools such as Z39.50 and WorldCat. And as cataloguing departments see decreases in staff resources but increases in the number of titles requiring access for users, they are forced to consider new ways of managing catalogue records. Like many other institutions, the University of Calgary has chosen to use external sources of catalogue records for electronic books. It has become evident from doing so that publishers, academic libraries, vendors, and library service providers need to collaborate on an expanded scale in order to ensure sustainable workflows for academic institutions and the best possible digital access for users. This paper covers the challenges that the University of Calgary has faced with electronic book cataloguing and digital access and its new-found success in managing these activities by partnering with Serials Solutions, Yankee Book Peddler (YBP), and ebrary. The focus is on the collaborative efforts made by all of these parties to make electronic resources available on a mass scale through the library catalogue and beyond.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".