Bibliographic record
Abstract
Building on the 2019 ACRL/SPARC Forum on Collective Reinvestment in Open Infrastructure, this program will explore how libraries can make different commitments to fund content created by open infrastructures. Library collections increasingly promote and reflect such open content and many have chosen to contribute to funding those products. There is not one formula or roadmap to underwrite the publishing and distribution costs of these open resources. There are many variables and considerations as some open content corresponds to serials and others are books or monographs. Open access content is increasingly found in nearly all subject areas, as scholarly publishing models have evolved. Open access does not come without a price to create, maintain and preserve the outputs. Libraries are reconsidering whether they want to commit so much to purchase materials or subscription-based products, when it is unclear what the anticipated use of any materials will be over time. Planning and opportunities for new and more flexible decisions concerning adjustments to and expenditures of the materials budget are under exploration by libraries. There are many options to invest in creating more content to be released as open access. Such options include contributing financially from the Library collections or materials budget to subsidizing or covering APCs, engaging in a more “library as publisher” model hosting journals, publishing books, creating OERs, and offsetting other expenses that ultimately drive a more intensive open infrastructure. Library leaders and partners will share their ideas about trying different approaches to contribute to more open publishing initiatives and explore whether efforts in deploying current book and serial costs to offset opportunities to build a wider and more open infrastructure is on the horizon. This analysis should incorporate the costs of analytical tools necessary to the use of such content in today’s research. Questions will be solicited ahead of time to reflect audience’s interest in such a rethinking of the library collections budget. Please email Julia Gelfand at with your questions.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.032 | 0.035 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".