A Benchmarking Survey of Open Access Funds at the University of California
Bibliographic record
Abstract
Objective – The purpose of this study was to examine the status and viability of application-based open access funds (OAFs) across the University of California (UC) Libraries to assist with long-term planning for this type of funding at UC. Methods – In 2022, the authors surveyed the 10 UC campus libraries about both the outcome of an earlier UC-wide OAF pilot and the current status of application-based OAFs to support article processing charges (APCs), book processing charges (BPCs), and open educational resources (OERs). Five campuses reported having a current OAF. These five campuses responded to additional questions about their budgets and their sustainability, the number of publications funded, policies, and staffing resources for managing the OAF. Results – Five UC campuses had an active application-based OAF, with budgets or expenditures ranging from $20,000 - $271,000 annually. Only two campuses felt their budget was sustainable. One of the five campuses closed its fund after the survey. The number of staff resources per fund ranged from 1 to 6 with 3 to 32 hours of work weekly. Funding policies were similar to other institutional OAFs with some distinctions. All campuses had revised their criteria to disallow funding for journals covered by UC’s transformative open access agreements. Conclusion – Providing application-based funds for OA publishing at high-publishing academic institutions requires a substantial budget and workforce. Though these funds benefit some authors, the wider equity of APCs and BPCs needs to be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.194 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".