Language, law and equality : proceedings of the Third International Conference of the International Academy of Language Law (IALL) held in South Africa, April 1992
Bibliographic record
Abstract
I. Introduction. Opening statement by the Mayor of Pretoria / Advocate J.H. Leach (pages 1-4) -- The importance of the conference theme: 'language and equality' / Joseph-g. Turi (pages 5-24) -- II. Language and law : theory and practice. Language, law and equality in South Africa : a theoretical and practical challenge / Dawid van Wyk (pages 25-37) -- Implementing language rights in court : the role of the court interpreter in South Africa / N.C. Steytler (pages 38-56) -- Language in Canadian public law /Julius H. Grey (pages 57-89) -- 'Language rights, legal rights and plain language' / Julien hofman (pages 90-102) -- III. Language rights and human rights. The language of negotiation, and of a post-apartheid constitution : the choice between expressivism/nihilism and formalism/determinism as language law and legal theoretical frameworks / N.J.C. van den Bergh (pages 103-105) -- Language, ethnicity, territory : language rights from human rights to international public law / Roland Breton (pages 106-122) -- Language rights and human rights / P.J.J. Olivier (pages 123-128)
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".