The case of ‘losses in any event’: a question of duty, cause or damages?
Bibliographic record
Abstract
This paper considers the relevance of a finding that, even absent the defendant's unlawfulness, the private law claimant would have suffered the losses claimed. It provides a principled framework for considering the issues raised by such a finding of ‘losses in any event’, arguing that it should be distinguished both from causation of injury and from the scope of the defendant's duty of care, and that it should be treated as raising a question of damages. It highlights the need, particularly in pure economic loss cases, for a careful comparison of the real and the hypothetical losses so as to determine whether the latter would indeed have been losses in any event. In this regard, the decision of the Court of Appeal inCalvert v William Hill Credit Ltdis subjected to close scrutiny. A more general argument advanced is that tort and contract both do and should adopt similar approaches in this field.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.022 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".