The right to language in court: A language right or a communication right?
Bibliographic record
Abstract
About a year ago a report appeared in the South African print media (Ngalwa 2007) under the heading Language policy in courts in the dock. It highlighted the fact that of the eleven official languages in South Africa only two, English and Afrikaans, remain the languages of record in the country's courts, while the other languages are translated by court interpreters. In comparison with language rights in the education system, which are continually debated, both in South Africa (Heugh 2002, Malherbe 2004, Webb 2006, Webb 2007) and internationally (Skutnabb-Kangas 2000, Skutnabb-Kangas 2002), language rights in the judiciary receives relatively little attention. In this contribution some arguments offered for the trail on the language policy in courts will be discussed. Especially with reference to court cases in South African and Canadian judicature, the importance of language in the right of an accused to a fair trial, will be commented on. In the first section of the contribution a short historical overview of the language requirements relating to the South African judiciary will be given, followed by a discussion of the two main arguments, viz. the right to language use in court is a communication right versus the view that it is a language right.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".