Patron-Driven Acquisition of E-Books Satisfies Users’ Needs While Also Building the Library’s Collection
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
Objective – To present the initial results of an academic library’s one-year pilot with patron-driven acquisition of e-books, which was undertaken “to observe how user preferences and the availability of e-books interacted with [the library’s] traditional selection program” (p. 469). 
 
 Design – Case study.
 
 Setting – The University of Iowa, a major urban research university in the United States.
 
 Subjects – Original selection of 19,000 e-book titles from ebrary at the beginning of the pilot in October 2009. To curb spending during the pilot, the number of e-book titles available for purchase was reduced to 12,000 titles at the end of December 2009, and increased to nearly 13,000 titles in April 2010. 
 
 Methods – These e-book titles were loaded into the library’s catalogue. The goal was for the University of Iowa’s faculty, staff, and students to search the library catalogue, discover these e-book titles, and purchase these books unknowingly by accessing them. The tenth click by a user on any of the pages of an e-book caused the title to be automatically purchased for the library (i.e., ebrary charged the library for the e-book). 
 
 Main Results – From October 2009 to September 2010, the library acquired 850 e-books for almost $90,000 through patron-driven acquisition. The average amount spent per week was $1,848 and the average cost per book was $106. Researchers found that 80% of the e-books purchased by library patrons were used between 2 to 10 times in a 1-year period. E-books were purchased in all subject areas, but titles in medicine (133 titles purchased, 16%), sociology (72 titles purchased, 8%), economics (58 titles purchased, 7%), and education (54 titles purchased, 6%) were the most popular. Two of the top three most heavily used titles were standardized test preparation workbooks. In addition, 166 of the e-books purchased had print duplicates in the library, and the total number of times the print copies circulated dropped 70% after the e-versions of these books were obtained.
 
 The authors also examined usage data for their subscription to ebrary’s Academic Complete collection from September 2009 to July 2010, which consisted of 47,367 e-books. Together with the 12,947 book titles loaded into the catalogue for the patron-acquisition pilot, there were a grand total of 60,314 ebrary e-book titles in the library catalogue that were accessible to the Iowa University community. The study revealed that 15% of these titles were used during this 11-month period, and the used titles were consulted 3 or more times. The authors sorted the user sessions by publisher and found that patrons used e-books from a wide variety of publishing houses, of which numerous university presses together constituted the majority of uses. The five most heavily used e-books were in the fields of medicine, followed by economics, sociology, English-American literature, and education. 
 
 Conclusion – The authors’ experience has shown that patron-driven acquisition “can be a useful and effective tool for meeting user needs and building the local collection” (p. 490). Incomplete coverage of academic publications makes patron-driven acquisition only one tool among others, such as selection by liaison librarians, which may be employed for collection development. According to the authors, patron-driven acquisition “does a good job of satisfying the sometimes 
 unrecognized demand for interdisciplinary materials often overlooked through traditional selection methods,” (p. 491) and alerts librarians to new research areas.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.426 |
| 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 it