The Power of Apology: Mercy, Forgiveness or Corrective Justice in the Civil Liability Arena (2007) Vol 1 Art 5
Bibliographic record
Abstract
The recent rash of apology-protecting legislation in tort law in the common law world raises interesting questions about why apologies are so important. The function of apologies within society generally is not absolutely clear. It is even less clear what their function in relation to civil liability is and how the relationship between the law and apologies works. It is fairly clear that legislators desire apologies to reduce litigation on the basis of some naïve view that that is what people really want and that the common legal advice to never apologise is actually very bad for society in general. In this paper I argue first that defining apologies is crucial to determining their function, that apologies have multiple functions and that one of them is corrective justice. Another is to mediate relationships and to achieve reconciliation or healing through a process of apology, forgiveness and redemption. When should an apology be protected and why can only be answered if we have a real understanding of both the psychological and sociological effects of apologies. In particular we need to understand the interactions of different types of norms, including norms of civility, legal norms, professional ethics and so on. The article attempts to go some way towards this understanding.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".