Library Supported Open Access Funds: Criteria, Impact, and Viability
Bibliographic record
Abstract
Abstract Objective – This study analyzes scholarly publications supported by library open access funds, including author demographics, journal trends, and article impact. It also identifies and summarizes open access fund criteria and viability. The goal is to better understand the sustainability of open access funds, as well as identify potential best practices for institutions with open access funds. Methods – Publication data was solicited from universities with open access (OA) funds, and supplemented with publication and author metrics, including Journal Impact Factor, Altmetric Attention Score, and author h-index. Additionally, data was collected from OA fund websites, including fund criteria and guidelines. Results – Library OA funds tend to support faculty in science and medical fields. Impact varied widely, especially between disciplines, but a limited measurement indicated an overall smaller relative impact of publications funded by library OA funds. Many open access funds operate using similar criteria related to author and publication eligibility, which seem to be largely successful at avoiding the funding of articles published in predatory journals. Conclusions – Libraries have successfully funded many publications using criteria that could constitute best practices in this area. However, institutions with OA funds may need to identify opportunities to increase support for high-impact publications, as well as consider the financial stability of these funds. Alternative models for OA support are discussed in the context of an ever-changing open access landscape.
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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 | 0.081 | 0.349 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.045 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".