A Tale of Two Serials Cancellations
Bibliographic record
Abstract
Beginning in 2016, both Western Washington University (WWU) and George Washington University (GWU) found that they needed to make significant and similar reductions in continuations costs over the next five years. In response, this past year, both institutions took independent, significant steps toward these ends, developing systematic, sustainable procedures for addressing these reductions. The approaches taken by the two institutions will be compared and contrasted in this presentation, particularly with respect to the following questions, which both libraries encountered: What defines a successful cancellation process in 2016? What are the most effective approaches to cancelling serials? When do cancellations do ”least harm” to students and faculty? After cancellations, how is access to content affected to the smallest degree possible? Did the cancellation process have the appearance of fairness to stakeholders? How does a library foster university buy-in? What do successful negotiations with publishers look like? Members of the team will discuss: Criteria for possible retention or cancellation Different assessment methods utilized Communication with subject liaisons and disciplinary teams Outreach to and response from faculty The panel will also address lessons learned from their efforts, as well as future plans in a continuing flat budget scenario.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".