Copyright Amendment Bill: Contradictions to Hit the South African Education Sector
Bibliographic record
Abstract
This article maps the latest developments in South Africa’s complex battle to update its Copyright Amendment Bill across a path strewn with legal pitfalls. Driving the agenda of the American-derived “fair use” and other copyright exceptions at the expense of content creators are the state, under the guise of “access” to education, and Big Tech companies focused on data mining, paraded as users’ rights to content. The emerging Bill has given rise to a set of major contradictions that will directly and negatively impact especially educational book publishing, from primary to tertiary sectors. The updated Bill risks violating authors’ rights and international treaties. The authors identify contradictions in public policy and sketch the most contentious aspects within debates around the Bill. The implications for the national research economy are considered, while the need to adequately protect the copyright of open access content is raised. The article closes with a summary of the issues of “fair use” and fair dealing, the predatory implications, and the outcome of the contradictions for the industry. The relevance of writing about a moving target is because a) the Bill has been in contestation for eight years now; b) universities and the whole educational sector have failed to respond coherently to the threats portended in the Bill; c) the nature of the claims and counter-arguments raised by the Bill will continue well after it has been promulgated; and d) the analysis is alert to open access imperatives and to the threat of South Africa becoming a haven for servers hosting pirated content should the Bill become law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".