Taking the Long View: A Case Study of E-Book Usage at a Comprehensive Research University
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
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it