Comparative analysis on definitions and types of apologies in apology legislation in the UK, Republic of Ireland, Australia, Canada, the USA and Hong Kong / Nurul Shuhada Suhaimi, Haswira Nor Mohamad Hashim and Noraiza Abdul Rahman
Bibliographic record
Abstract
This study compares the definitions and types of apologies adopted by apology legislation in selected Common Law jurisdictions i.e., England and Wales in the UK, Republic of Ireland, the states of New South Wales, Victoria and Queensland in Australia, Canada, the USA and Hong Kong. The apology legislative reform undertaken by these jurisdictions provides a solution to the long-standing problem of adverse legal effects of apology. A similar problem is reported in Malaysia due to similarities in evidentiary rules, insurance contract clauses and statutory limitation law attributed to the Common Law system. The objective of this paper is to comparatively analyses two scopes of the apology legislation, i.e., the definition and the type of apology adopted in the apology legislation by the selected Common Law jurisdictions. Based on the comparative analysis, this paper finds that there are three categories of the definition i.e., apology definition that includes acknowledgement of fault, apology which excludes acknowledgment of fault, and no definition of apology in the legislation. This paper also finds that there are two categories of types of apologies in the apology legislation in the analysed jurisdictions, i.e. full apology and partial apology. The findings of this paper help towards the development of the law that protects admission by apologetic discourse in Malaysia.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".