Taking the Long View: A Case Study of E-Book Usage at a Comprehensive Research University
Bibliographic record
Abstract
The University Libraries at Virginia Tech made their first major acquisition of e-books in 2008 with a purchase of new e-book collections from Springer. While the business relationship has evolved over time, it has continued forward to the present day. Currently, the library’s online holdings include most of the frontlist subject collections available from what is now Springer Nature, as well as the Springer book series and the Springer Book Archives. In all, the University Libraries make over 120,000 e-books available to patrons through the SpringerLink platform. The cumulative usage of this material represents over two million chapter downloads by the university community just since 2012. The large number of titles available and the long-term nature of the acquisitions provide unique opportunities for in-depth analysis. The Springer Nature e-book collections also offer a variety of material types including monographs, textbooks, and reference works integrated onto the same platform. This session provides a case study of Springer Nature e-book usage at Virginia Tech and shows how working directly with a vendor partner can provide an enhanced and more multifaceted view of usage.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".