Ebooks Versus Print Books: Format Preferences in an Academic Library
Bibliographic record
Abstract
When a scholarly monograph is made available in both print and electronic formats, which format will users prefer? This study analyzed monograph usage data from three university presses in the University of Toronto Libraries' collections, comparing print and ebook usage patterns of identical titles. The goal was to examine format preferences and determine whether there are differences in usage across subject disciplines or publishers. The study showed that although in many cases users preferred one format over another, they used books in both formats. If a subject was popular, usage tended to be high for both formats, and if unpopular, low for both formats. The data also indicated that there were some noticeable differences in ebook usage for particular subjects, and the authors concluded that format does matter and therefore it is desirable for libraries to provide both formats if possible. The study also highlighted how critical metadata are in promoting the use of electronic resources. If there were no ebook metadata within the library catalog, the ebook usage was low. This analysis adds to a growing body of literature in user preferences on book formats that can assist libraries in making better-informed decisions in collection building.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".