Quantification of damages for malicious prosecution: A comparative analysis of recent South African and Commonwealth case law (2)
Bibliographic record
Abstract
The first part in this three-part submission was devoted to a number of preliminary issues relating to the assessment of damages for malicious prosecution, such as the confusion caused by the word ‘damage’ as an element in the law of malicious prosecution and ‘damages’ in terms of the amount a successful plaintiff in an action for malicious prosecution could recover. In that part also, opportunity was taken to explore the circumstances where damages have been awarded in an action for malicious prosecution in South Africa and the ascertainment of the amount in such instances, with the tip of the iceberg being the recent case of the former Judge President of KZN High Court. The present article continues with the case study approach by investigating the experiences of Australia, Canada and Trinidad and Tobago having identified instances where damages has been awarded for the tort of malicious prosecutions in those common law jurisdictions. The cases discussed with regard thereto are no doubt informative but, the Privy Council judgment from Trinidad and Tobago is by far more instructive and dynamic on the factors to be taken into account in the quantification of damages for malicious prosecution from the point of view of the ordinary member of the society. Thus, the reputational damage which the law protects by an award of damages does not distinguish between a homeless person and an urban or rural dweller.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".