Exploring state and institutional support for sustainable scholarly journal publishing
Bibliographic record
Abstract
The study aims to identify global practices of financial and non-monetary support for scholarly journals, funding criteria, and associated outcomes. An exploratory review retrieved 438 documents from Scopus, Web of Science, and Research4Life, 28 of which were selected for thematic content analysis. Data were categorized into eight micro-themes, including funding schemes, infrastructure, and journal evaluation criteria. The findings reveal six key models of support: (1) public grants at the state level, (2) program-based funding at the state level, (3) national infrastructure/platform support, (4) consortia-based funding, (5) direct institutional funding from publishers or parent organizations, and (6) institutional non-monetary or in-kind support. These models vary across regions and are often combined. Countries with stable national funding and infrastructure (e.g., Finland, Poland, Canada) show higher journal sustainability and indexing success. In contrast, journals in resource-limited settings often rely on volunteer work and institutional goodwill. A noteworthy trend is thematic and language-based targeting. For example, Taiwan prioritizes technology journals, Canada’s SSHRC supports social science journals, and Quebec programs only support French-language journals. Academic libraries contribute to sustainability through infrastructure, metadata services, and policy support.
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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 | 0.034 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".