Barriers and benefits of transitioning to an equitable open access model: interviews with LIS journal editors
Bibliographic record
Abstract
Not all library and information science (LIS) journals operate under an equitable open access (OA) model, despite librarianship’s emphasis on OA as a value. A previous study illuminated significant barriers for journal editors in transitioning to an equitable OA model. This follow-up study sought, through structured interviews with editors of unflipped journals, to further explore these barriers in order to identify themes and potential solutions to overcome barriers. LIS journal editors who oversaw a transition to equitable OA models were also interviewed regarding the process and impact of flipping the journal. Through qualitative analysis of these interviews, several themes emerged. Barriers to flipping include lack of individual or journal-level agency and motivation, as well as competing priorities for the editors, journal or organization. Benefits to flipping included better alignment of personal and organizational values, increases to perceived prestige and lack of membership disruption for professional societies. Analysis also demonstrated ways in which barriers to flipping differ between professional society journals and large for-profit publisher journals. Based on the analysis, several next steps were identified to better support LIS and other journal editors, as well as potentially move toward solutions to successfully transition more journals in the future.
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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 | 0.029 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".