How much of library digital content is checked out but never used?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to identify patterns, trends and potential implications related to post-checkout non-usage (material that is checked out by a user, but subsequently never opened and/or downloaded) of library digital content. Design/methodology/approach A large urban Canadian public library’s data (2013-2017) from Rakuten OverDrive was analyzed. Pending items (items that are checked out, but neither opened nor downloaded) were compared with total checkouts to determine post-checkout non-usage rates. Findings Checkouts and overall rates of post-checkout non-usage of e-books and e-audiobooks have risen significantly and consistently. Juvenile and non-fiction e-books demonstrate higher post-checkout non-usage rates than adult and fiction e-books, respectively. The library spends up to US$10,700 per year on metered access e-books that are never opened by users. This number has grown significantly over the years. Originality/value E-materials in libraries have been growing rapidly, but their current lending models are still largely a direct application of concepts in traditional library services that have developed based on physical materials, such as checkouts, due dates, renewals, holds and wait times. However, e-materials do not have the limitation of physical materials that prevents other users from accessing a checked-out item, which makes many of the traditional concepts no longer applicable. New concepts and lending models should be developed that allow users to access any library e-materials at any time, and are financially functional and sustainable for both libraries and e-content providers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.020 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it