Analysis of Static and Dynamic E-Reference Content at a Multi-Campus University Shows that Updated Content is Associated with Greater Annual Usage
Bibliographic record
Abstract
A Review of: Lamothe, A. R. (2015). Comparing usage between dynamic and static e-reference collections. Collection Building, 34(3), 78-88. http://dx.doi.org/10.1108/CB-04-2015-0006 Abstract Objective – To discover whether there is a difference in use over time between dynamically updated and changing subscription e-reference titles and collections, and static purchased e-reference titles and collections. Design – Case study. Setting – A multi-campus Canadian university with 9,200 students enrolled in both graduate and undergraduate programs. Subjects – E-reference book packages and individual e-reference titles. Methods – The author compared data from individual e-reference books and packages. First, individual subscription e-reference books that periodically added updated content were compared to individually purchased e-reference books that remained static after purchase. The author then compared two e-reference book packages that provided new and updated content to two static e-reference book packages. The author compared data from patron usage to new content added over time using regression analysis. Main Results – As the library acquired e-reference titles, dynamic title subscriptions added to the collection were associated with 2,246 to 4,635 views per subscription while static title additions were associated with 8 to 123 views per purchase. The author also found that there was a strong linear relationship between views and dynamic titles added to the collection (R2=0.79) and a very weak linear relationship (R2=0.18) with views when static titles are added to the collection. Regression analysis of dynamic e-reference collections revealed that the number of titles added to each collection was strongly associated with views of the material (R2=0.99), while static e-reference collections were less strongly linked (R2=0.43). Conclusion – Dynamic e-reference titles and collections experienced increases in usage each year while static titles and collections experienced decreases in usage. This indicates that collections and titles that offer new content to users each year will continue to see growth in usage while static collections and titles will see maximum usage within a few years and then begin to decline as they get older. Fresh content is strongly associated with usage in e-reference titles, which mirrors the author’s previous work examining static and dynamic content in e-monographs.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".