Comparison of E-Book Acquisitions Strategies Across Disciplines Finds Differences in Cost and Usage
Bibliographic record
Abstract
A Review of: Carrico, S.B., Cataldo, T.T., Botero, C., & Shelton, T. (2015). What cost and usage data reveals about e-book acquisitions: Ramifications for collection development. ALCTS, 59(3). Retrieved from https://journals.ala.org/lrts/article/view/5752/7199 Abstract Objective – To compare e-book cost-usage data across different acquisitions styles and disciplines. Design – Case study. Setting – A public research university serving an annual enrollment of over 49,000 students and employing more than 3,000 faculty members in the Southern United States. Subjects – Cost and usage data from 15,006 e-books acquired by the Library through packages, firm orders, and demand-driven acquisitions. Methods – Data was collected from publishers and vendors across the three acquisitions strategies. Usage, cost, and call number information was collected for the materials purchased via firm order or demand driven acquisitions and these were sorted into disciplines based on the call number assigned. Discipline, cost, and use were determined for each package collection as a whole because information on individual titles was not provided by the publishers. The authors then compared usage and cost across disciplines and acquisitions strategies. Main Results – Overall, e-books purchased in packages had a 50% use rate and an average cost per use of $3.39, e-books purchased through firm orders had a 52% use rate and an average cost per use of $22.21, and e-books purchased through demand driven acquisitions had an average cost per use of $8.88 and 13.9 average uses per title. Package purchasing was cost effective for science, technology, engineering, and mathematics (STEM) materials and medicine (MED) materials. Demand driven acquisition was a particularly good strategy for humanities and social sciences (HSS) titles. Conclusion – There are differences between the acquisitions strategies and disciplines in cost and use. Firm orders had a higher cost per use than the other acquisitions strategies.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".