Financial and Other Perceived Barriers to Transitioning to an Equitable No-Publishing Fee Open Access Model: A Survey of LIS Journal Editors
Bibliographic record
Abstract
About half of Library and Information Science (LIS) journals continue to charge authors to publish articles open access (OA) or do not offer OA publishing at all. To further investigate the financial and other perceived barriers preventing these LIS journals from transitioning to no-publishing fee OA models, this exploratory project surveyed lead editors for each identified LIS journal. Results indicate most of the journals have not discussed transitioning to a no-publishing fee OA model, and that finances are the main barrier. Most also indicated a lack of awareness of their journal’s budget. The most popular no-publishing fee OA model was Subscribe to Open.
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 | 0.079 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.047 | 0.159 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.124 | 0.027 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".