“I Am in a Privileged Situation”: Examining the Factors Promoting Inequity in Open Access Publishing
Bibliographic record
Abstract
ABSTRACT Despite increasing advocacy for open access (OA), the uptake of OA in some disciplines has remained low. Existing studies have linked the low uptake in OA publishing in the humanities and social sciences (HSS) to disciplinary norm, limited funding to pay for article processing charges (APCs), and researchers' preferences. However, there is a growing concern about inequity in OA scholarly communication, as it has remained inaccessible and unaffordable to many researchers. This study therefore investigated inequity in OA publishing in Canada. Using semi‐structured interviews, qualitative data was collected from 20 professors from the HSS disciplines of research‐intensive universities in Canada. Data was analyzed with NVivo software following the reflexive thematic analysis approach. Findings revealed three main causes of inequity in OA publishing among the participants. These are the cost of APCs, unequal privileges, and gender disparities. Hence, there is a need for concerted efforts by funding agencies, stakeholders, higher education institutions, and researchers to promote equity in OA scholarly communication. Some recommendations for improving equity in OA publishing are provided in this paper.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".