Bibliographic record
Abstract
The 1997 formulation of the test in Harksen by Constitutional Court seemed to settle the constitutional standards in respect of equality and unfair discrimination. In this article this test is scrutinised closely with a view to clarify the different aspects of the right protected in s 9. Reliance is further placed on Canadian commentary which interrogates the dignity-centred analysis of the Canadian Supreme Court in relation to the Canadian Charter's prohibition of discrimination. The similar insistence on dignity as the interest protected by the right to equality opens up new possibilities in the South African context. Accordingly, specific 'wrongs' or 'indignities' of inequality are identified, which refines the Harksen test. The identification of the indignities, it is suggested, assists litigants and the court on a practical level when determining the fairness or otherwise of discrimination.
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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 | 0.032 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.074 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".