When "Sorry" is the Hardest Word to Say, How Might Apology Legislation Assist?
Bibliographic record
Abstract
Apology legislation refers to statutory provisions that remove legal disincentives to offering an apology in the context of civil disputes. The legislation clarifies and, in many cases, alters what would otherwise be the legal consequences of an apology, principally by reforming the law of evidence. A principal aim of apology legislation is to encourage apologies by removing legal disincentives to apologising. Other aims are to promote the settlement and resolution of disputes and to reduce litigation. Apology legislation has been enacted in most US states, each state and territory in Australia, in England and Wales, in most Canadian provinces and territories, and has been considered in Scotland. Apology legislation is currently being considered by the Department of Justice, Hong Kong Special Administrative Region Government. This article identifies a number of matters that need to be considered when introducing apology legislation to assist in the resolution of legal disputes.
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.
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 | 0.052 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".