Breaking-up is hard to do: A unique methodology for unbundling a âBig Dealâ
Bibliographic record
Abstract
Academic libraries acquire access to many journal titles through âBig Dealâ bundles. As serials prices continue to rise at unsustainable rates it will become increasingly necessary to consider breaking-up these packages and just subscribing to the most important titles individually. Recently, it appeared that the University Library, University of Saskatchewan would likely no longer be able to afford the American Chemical Society (ACS) bundle of 40+ titles, and tough decisions would need to be made. Usage data on each title were readily available â but is that enough evidence? Working under the common assumption that the primary users of this package are the Chemistry Department researchers, a citation analysis was conducted on what ACS journals these users recently published in and cited in their articles. In an effort to engage chemistry researchers and offer them a voice in the process, a survey of their opinions on each ACS title was also conducted. It was hoped that combining data from these three discrete sources: usage statistics, citation analyses, and user feedback, would enable us to arrive at the most conscientious, evidence-based decisions possible. This study took the novel approach of applying a citation analysis technique to usage data and survey responses. Although unconventional, this unique methodology proved useful in this situation. This presentation will describe the steps taken and discuss the benefits and challenges of this method so that librarians may consider whether this approach could be adapted to their own collections analysis needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".