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Record W1511159194 · doi:10.18438/b8b891

Preference for Print or Electronic Book Depends on User’s Purpose for Consulting

2014· article· en· W1511159194 on OpenAlexaffvenue
Laura Newton Miller

Bibliographic record

VenueEvidence Based Library and Information Practice · 2014
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsCarleton University
Fundersnot available
KeywordsPreferenceReading (process)The InternetUsabilityLibrary scienceWorld Wide WebCollection developmentComputer sciencePsychologyPolitical science

Abstract

fetched live from OpenAlex

A Review of: Rod-Welch, L.J., Weeg, B.E., Caswell, J.V., & Kessler, T.L. (2013). Relative preferences for paper and for electronic books: Implications for reference services, library instruction, and collection management. Internet Reference Services Quarterly, 18(3-4), 281-303. doi: 10.1080/10875301.2013.840713 Abstract Objective – To determine patron format preference, perceived usability and frequency of e-book usage, and to study use and preference of e-reading devices. Design – Survey questionnaire. Setting – Large public research university in the United States of America. Subjects – 339 students, faculty, and staff members Methods – An anonymous 23-item survey was available in online and print formats. Print surveys were distributed in the lobby of the library and throughout various buildings on campus. A direct link to the online version of the survey was included in e-newsletters, on the library homepage, and on the library’s Facebook site. A definition of e-book was placed prominently at the beginning of the survey. Questions included information on preference of format (11), experiences using e-books (3), ownership of particular devices for reading e-books (1), attitudes regarding library purchase of e-books and readers (3), demographic information (4), and additional comments (1). Main Results – Of the 339 completed surveys, 79 were completed online and 260 in print. When asked about preference in format for reading, 79.6% of respondents preferred print books compared to 20.4% choosing e-books. If the library was purchasing a book to support class research and projects, 53.9% preferred print and 46.1% preferred electronic, but if the library purchased a book for leisure reading, 76% preferred print and 24% preferred electronic. In response to the question about how often they used e-books from the library, 50.1% of respondents never used library e-books, 21.1% used once per year, 20.8% monthly, 7.4% weekly and 0.6% daily. Of those who used e-books, 38.1% read only sections they needed, 31% searched keywords, 24.2% downloaded and printed pages to read later, 21.8% read the most relevant chapters, 17.1% skimmed the entire book and 14.2% read the entire book. If both formats were available, 25.1% felt that the library should purchase the print book, 16.7% the e-book, and 58.2% chose both formats. When asked about downloading e-books, 51.1% of respondents would use an e-book only if they could download it to a hand-held device. A majority of the respondents, 81.7%, felt that the library should provide e-readers for checkout if the library purchased e-books instead of print books. When asked which types of books they preferred to read in electronic format in an open-ended question, 22% preferred textbooks, 21% leisure reading, 18% research books, 15% other types, 6% journals, 5% reference books, and 3% anything. Regarding which types of books were preferred in print format, 42% preferred leisure reading, 21% other, 14% all, 11% textbooks, 6% research books, 2% no e-books, 2% journals and 2% reference books. Conclusion – Preference for book format (electronic or print) depends on the users’ purpose for reading the text. This will likely change over time, as users gain more familiarity and experience with e-books, and better support is provided from the library.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.918

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2740.128

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 head0.026
GPT teacher head0.249
Teacher spread0.223 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes2
Has abstractyes

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