E-Books Revisited: Surveying Student E-Book Usage in a Distributed Learning Academic Library 6 Years Later
Bibliographic record
Abstract
This article is the result of a 2009 survey of student usage of e-books at Royal Roads University (RRU) Library. The authors found that the proportion of students using RRU Library e-books has gone up since a similar 2003 survey, although only just above half of students are using RRU Library e-books. An almost identical number of student respondents (approximately 54%) said “no” to preferring a print version of the book over the e-book version of the book in both the 2003 and 2009 surveys. The majority of students rated the ability to download an e-book to a hand-held device as not important. There has been a notable increase in the use of e-books for course readings from 2003 to 2009. A lack of awareness of RRU Library e-books remains the top reason cited by students for not using them.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".