Can Law Protect Language? Law, Language and Human Rights in the South African Constitution
Bibliographic record
Abstract
Before the transition to full democracy in South Africa, only English and Afrikaans were official languages used in governmental matters. This effectively excluded more than half the population who had no working knowledge of those languages, from participation in public affairs and hindered them from accessing their entitlements. The 1996 South African Constitution set out to change this and to accord equality of official status to all the major languages — 11 in all, and placed a duty on government to take practical and positive measures to elevate the status and advance the use of formerly marginalised indigenous languages. A special body was set up to assist the government in carrying out this task. This paper examines the extent to which these provisions have been implemented, and examines the implications for equality, democracy and citizenship of the failure by government to carry out their constitutional mandate. The importance of making government and administration accessible to the population is explored and the paper suggests ways in which the 11 official languages could be used so that their use does not become too burdensome in terms of finance and administration. The paper draws on experience in these matters in Canada and a number of other countries which are multilingual. Finally the paper concludes that so little has been done to make the language provisions of the constitution workable, that the drift to English, discernible from 1993, has become virtually unstoppable.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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".