Academic Libraries Should Consider Deselection of Some Electronic Books
Bibliographic record
Abstract
A Review of: Waugh, M., Donlin, M., & Braunstein, S. (2015). Next-generation collection management: A case study of quality control and weeding e-books in an academic library. Collection Management, 40(1), 17-26. http://dx.doi.org/10.1080/01462679.2014.965864 Abstract Objective – To describe and advocate for the development of a procedure to discard electronic books from an academic library collection. Design – Case study. Setting – Academic library in the United States of America. Subjects – 514 electronic books purchased from NetLibrary, a subset of 52,000 NetLibrary titles collected by the investigating library 2001-2007. Methods – The researchers examined a set of 514 electronic books in the health sciences and medical field, specifically for qualities such as currency and content relevance. An anecdotal case with limited validity, the goal was to articulate why a particular set of electronic books failed to meet the investigating library’s collection standards, and to remove these e-books. Main Results – A set of 514 e-books published by ICON Health Publications were found to be mass-produced, and displayed other notable problems, including age over seven years, outdated or irrelevant content, quality issues, and inclusion in an older platform no longer favored for e-books. The ICON Health e-books were removed from the library collection and, with some difficulty, the items were also removed from the vendor platform. The authors recommended an e-book weeding procedure that considers six potential problems: publication date; inclusion of defunct Internet links; mass production; low quality works by the same authors or publishers; e-book packages that appear to feature multiple low quality works; and e-books from early packages, which may have integration problems. Conclusion – Electronic books may take up little physical space but libraries should not ignore them when making deselection decisions because their content may be inappropriate for a library or for the disciplines the library serves. The ICON Health Publications e-book package is an egregious example of low-quality e-book content that the authors discovered and subsequently removed from their collection, offering a set of recommendations based on the experience.
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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 | 0.038 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.035 | 0.031 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.042 |
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".