Assessing the Demand for Scholarly Publishing in South Africa
Bibliographic record
Abstract
It is often assumed that African scholarly publishing is poorly resourced and unappealing to authors, both African and otherwise. While the resource constraints are real, is it true that authors are avoiding African publishers in favour of those based in the North? An exploratory study was undertaken to assess the demand for scholarly publishers based in South Africa but serving the continent as a whole. The qualitative survey, though not based on a large and widely generalizable sample, indicates some clear trends and, in so doing, explodes the myth of the African publisher as the site of last resort for academic authors. Rather, the results show broad support for locally based publishers, and many respondents expressed a need for far more publishers, wherever they may be based. Certain guidelines are derived from this survey to indicate trends for scholarly publishers working in this region.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.490 | 0.647 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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".