Development of Deal- and Journal-level Metrics and Methods Assists Librarians to Evaluate Big Deals
Bibliographic record
Abstract
A Review of: Blecic, D.D., Wiberley, Jr., S.E., Fiscella, J.B., Bahnmaier-Blaszczak, S., & Lowery, R. (2013). Deal or no deal?: Evaluating Big Deals and their journals. College & Research Libraries, 74(2), 178-193. Objective – To assess the value of aggregated journal packages (Big Deals) and to select individual journal titles for continued subscription should a deal be cancelled. Design – Case study. Setting – Doctoral research university library in the United States of America. Subjects – Three anonymous Big Deals. Methods – The authors define metrics at two levels (deal and journal) to evaluate Big Deal packages. The metrics rely heavily on the COUNTER JR1 metric Successful Full-Text Article Request (SFTAR). Main Results – The authors found that while 30% of journals provide 80% of SFTARs, the cost of subscribing to these journals individually would not save significant sums of money. Additionally, they speculate that library users would increase the number of interlibrary loan requests to access the 20% of SFTARs that would be inaccessible if a Big Deal was cut, amounting to increased costs. Conclusion – With no sign of publishers moving to change the price and conditions of Big Deals, these arrangements are becoming unsustainable for libraries. As this occurs, librarians require methods of assessing which deals to keep and which to cut, as well as evidence of to which individual journals they should subscribe. The authors of this paper set out one method of conducting these assessments that they have found to be useful at an academic library. They conclude by stating that even with SFTAR data, individuals must keep in mind the necessity of providing equitable access to all of a university community’s user groups.
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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 | 0.182 | 0.411 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.040 | 0.042 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".