Strengthening scholarly publishing in Africa : assessing the potential of online systems
Bibliographic record
Abstract
This study investigated current publishing practices among scholarly journals in Africa, while exploring the potential contribution of online publishing systems to aid those practices. This study examined how current systems, largely involving traditional publishing methods, offer Africans limited opportunities and incremental gains in taking advantage of faster and wider dissemination of digital systems for scholarly communication. Issues about authorship, readership, editorial and peer review, as well as the level of science resources in African academic libraries, were studied. Using a well-articulated, mixed-mode research design, this study has assembled data from 286 key actors – journal editors, potential journal editors, librarians, IT administrators, faculty and postgraduate students – from sub -Saharan Africa during a 12-month period in 2007–09. Drawing on this data set, this study documents and analyzes the unparalleled availability of journals and other information resources made available to the African research community through digital technologies and publisher policies, as well as current constraints in ICT infrastructure, training, and support inhibiting the utilization of these same technologies in advancing African scholarly publishing efforts. This study establishes the high level of energy and excitement among journals editors, librarians, and IT administrators about the compelling new possibilities offered by new digital technologies. Drawing on what has been learned in this study, recommendations are made for tapping into the full potential of these technologies in strengthening research capacity, improving the quality of research, reducing Africa’s isolation from the global scholarly community, and ultimately narrowing the information divide.
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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 | 0.022 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".