Bibliographic record
Abstract
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.
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 | 0.004 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".