Bibliographic record
Abstract
ABSTRACT The present study analyzed a large urban Canadian public library's data (2013‐2017) from Rakuten OverDrive 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. It was found that over 12% of the more than 1.1 million checkouts of digital content via OverDrive in 2017 were never opened, causing a waste of over $10,000 USD on metered access eBooks alone; this figure will likely increase in the coming years based on the trends found in this study. Juvenile and non‐fiction eBooks are most likely to be checked out and go unused. These findings may shed light on ways libraries and digital content vendors might improve the efficiency of digital content lending, and serve to inform collection management and user‐targeted marketing and solutions.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.012 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".