Open access publishing in an African context: Notable improvements and recurring challenges
Bibliographic record
Abstract
Open access publishing has been promoted as a pivotal means of bridging the gap in knowledge access and usage. Despite the growing support for open access publishing globally, little is known about African scholars’ engagement with open access publishing and the barriers limiting their open access publishing practices. Using a survey research design, data was collected from 241 researchers from selected universities in Africa, such as Nigerian, Kenyan and South African universities. The data was collected using online surveys and analysed using the descriptive statistics of frequency counts and percentages. The study reveals that while most of the respondents had published open access articles (78.01%) and had a positive perception of the quality of open access journals (73.45%) and editorial teams, more than half were still limited by article processing charges (58.51%) as they had no funding for their research. Although African researchers are embracing open access publishing more now than they were historically, barriers such as article processing charges and the prolonged response time from reviewers continue to pose a serious challenge to open access uptake in Africa. This study proposes five recommendations for improving open access uptake in African and Global South countries.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".