Bibliographic record
Abstract
There has been a tendency during the past 30 years, in many common law jurisdictions, towards an increase in amounts of damages, both in contract and tort. Arguments for increasing awards have, for a variety of reasons, been vigorously and effectively promoted, whereas the counter-arguments have appeared weak and diffuse. The counter-arguments, therefore, deserve attention. ‘The more the better’ cannot be a principle of justice, rationality, or of sound policy. The expansion of damage awards has been assisted by the ideas that the defendant is a wrongdoer deserving of little sympathy; that wrongs should all ideally be deterred, and so it is acceptable — desirable even — that damage awards should err on the side of excess; and that damages will in any event be paid by an anonymous insurance fund and impose a real burden on no one. The third idea is inconsistent with the others, and each of the three rests on erroneous assumptions. In many cases — probably in most cases — those liable to pay damages are not personally guilty of blameworthy conduct; it is not true that all conduct giving rise to what the law calls a wrong should ideally be deterred; and all awards, even if funded by insurance, have to be paid for. These points are illustrated by considering several kinds of legal wrongs, and several different kinds of loss.
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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".