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Record W3027909148 · doi:10.1111/hir.12310

A comparative study of medical ebook and print book prices

2020· article· en· W3027909148 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHealth Information & Libraries Journal · 2020
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedical libraryVendorLibrary scienceComputer scienceBusinessMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Although most medical libraries buy ebooks, there has been little discussion of the comparative costs of medical ebooks and print books. OBJECTIVES: To determine whether individually purchased medical ebooks cost more or less, on average, than the same titles in print format and, if so, to calculate the price differential. METHODS: The author searched the platform of monograph vendor YBP for the 1095 titles in the 'Clinical Medicine' category of Doody's Core Titles 2018 edition. For each title, the print price and the lowest ebook price were noted; the ratio of ebook price to print book price for each title was then calculated. RESULTS: On average, ebooks cost 2.20 times more than their print equivalents, though the size of the price differential varied greatly with the publisher. For some publishers, ebooks cost nearly the same amount as print books, while for others, ebooks cost three or even four times as much as the print. DISCUSSION: The greater price of some ebooks may make them unaffordable for libraries or mean that those titles cannot be purchased as ebooks even when that format would be preferred. CONCLUSIONS: Buying ebooks, at least on a title-by-title basis, can be very costly for medical libraries.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.011
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 head0.049
GPT teacher head0.282
Teacher spread0.233 · 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