E-Preferred Approval Books at the University of Manitoba: A Comparison of Print and Ebook Usage
Bibliographic record
Abstract
Abstract Objective – To compare the usage of print and ebooks received on University of Manitoba’s e-preferred YBP approval plan as well as to examine cost per use for the approval print books and ebooks. Methods – Usage data was compiled for books received on approval in 2012/2013 to December 31, 2014. Counter reports were used to determine use and non-use of ebooks, while vendor reports from EBL and ebrary were used for the cost per use analysis. Print usage information was drawn from SIRSI and then ALMA when UML switched systems at the beginning of 2014. Results – Ebooks received more use than p-books overall, but when examined by subject discipline, significant differences could not be found for the “STM” and “Other” categories. With ebooks, university press books tended to be used more than those from other publishers, but the same result was not found for print books. Ebrary ebooks tended to be used more often than EBL, EBSCO, and Wiley ebooks, and single-licence books tended to be somewhat more used than multi-user ones. Cost-per-use data was much lower for print books, though the comparison did not look at staffing costs for each medium. Conclusions – This study finds that of approval books matching the same profile, ebooks are used more, but print books receive more substantial use. Both formats are needed in a library’s collection. Future comparisons of cost per use should take into account hidden labour costs associated with each medium. Usage studies provide evidence for librarians refining approval plan profiles and for budget managers considering changes to monographic acquisition methods and allocations.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".