Scholarly publishing : The challenges facing the African university press
Bibliographic record
Abstract
This paper seeks to examine the challenges that face the university press in Africa in general and South Africa in particular. It will start by examining the state of the university press in Africa, the state of the university press in South Africa, the challenges that face university presses, such as the declining purchasing of scholarly monographs by university libraries since the budgets of most university libraries are now spend on subscribing to expensive journals and serials, poorly paid academic staff that does not purchase scholarly books, poor teaching and research infrastructure where the course pack has replaced the monograph in the classroom, a generally under-developed market, a weakly developed reading culture, short print-runs which are not economically viable, lack of distribution hubs such as bookshops and lack of intra-Africa book trade. Whereas in the past scholarly publishers could sell between 1000 and 1500 copies of a monograph, today they sell between 200-300 copies. Since publishing small print runs is not economically viable due to economies of scale, scholarly publishers are caught between a declining market and high costs involved in publishing small print runs. It will further examine the role that research institutes and science councils play in scholarly publishing and lastly it will examine the opportunities that new modes of communications offers to scholarly publishers.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".