Obtaining Journal Titles via Big Deals Most Cost Effective Compared to Individual Subscriptions, Pay-Per-View, and Interlibrary Loan
Bibliographic record
Abstract
A Review of: Lemley, T., & Li, J. (2015). "Big deal” journal subscription packages: Are they worth the cost? Journal of Electronic Resources in Medical Libraries, 12(1), 1-10. http://dx.doi.org/10.1080/15424065.2015.1001959 Abstract Objective – To determine if “Big Deal” journal subscription packages are a cost-effective way to provide electronic journal access to academic library users versus individual subscriptions, pay-per-view, and interlibrary loans (ILL). Design – Cost-per-article-use analysis. Setting – Public research university in the United States of America. Subjects – Cost-per-use data from 1) journals in seven Big Deal packages, 2) individually subscribed journals, 3) pay-per-view from publishers’ websites, and 4) interlibrary loans. Methods – The authors determined cost-per-use for Big Deal titles by utilizing COUNTER JR1 metric Successful Full-Text Article Request (SFTAR) reports. Individual journal subscription cost-per-use data were obtained from individual publishers or platforms. Pay-per-view cost was determined by recording the price listed on publishers’ websites. ILL cost-per-use was established by reviewing cost-per-article obtained from libraries outside of reciprocal borrowing agreement networks. With the exception of pay-per-view numbers, title cost-per-use was averaged over a three-year period from 2010 through 2012. Main Results – Cost-per-article use for journals from Big Deals varied from $2.11 to $9.42. For individually subscribed journals, the average cost-per-article ranged from $0.25 to $84.00. Pay-per-view charges ranged from $15.00 to $80.00, with an average cost of $37.72. Conclusion – The authors conclude that Big Deals are cost effective, but that they consume such a large amount of funds that they limit the purchase of other resources. The authors go on to outline the options for libraries thinking about Big Deal packages. First, libraries should keep Big Deal packages in place if the average cost-per-article is less than individual subscriptions. Second, libraries could subscribe only to the most-used journals in Big Deals, cancel the packages, and rely on ILL and pay-per-view access. Third, consortia could be joined to favourably negotiate Big Deal package prices. Fourth, Big Deals could be dropped completely. Fifth, individual libraries armed with JR1 reports can negotiate with publishers for better deals.
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".