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Record W2171773827 · doi:10.18438/b8n312

Academic Libraries Should Consider Deselection of Some Electronic Books

2015· article· en· W2171773827 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEvidence Based Library and Information Practice · 2015
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsCollection developmentLibrary scienceVendorComputer scienceQuality (philosophy)Relevance (law)Set (abstract data type)Library catalogWorld Wide WebPolitical scienceLaw

Abstract

fetched live from OpenAlex

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Publications were found to be mass-produced, and displayed other notable problems, including age over seven years, outdated or irrelevant content, quality issues, and inclusion in an older platform no longer favored for e-books.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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.515
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.267
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it