The Power of Apology: Mercy, Forgiveness or Corrective Justice in the Civil Liability Arena (2007) Vol 1 Art 5
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it