A Critical Economic and Comparative Analysis of the Doctrine of Remoteness of Damage in Contract and Its Functional Equivalents
Bibliographic record
Abstract
Hadley v Baxendale remoteness is generally regarded favourably in the law and economics literature. Orthodox theory views remoteness as an efficient rule, although its purported efficiency virtues vary. Most economic models portray remoteness as an information disclosure device which bridges information asymmetry and regulates rates of contracting, precautions against breach and even reliance by promisees. Yet the assumptions of economic models are denied by the content that courts attribute to the doctrine. This paper suggests that remoteness is an inefficient rule which entails certain costs, particularly through its impact on performance/breach decisions, but only uncertain and modest efficiency gains. It is argued that a more efficient default rule would allow full recovery of expectation damages. The paradigmchanging judgment in The Achilleas could pave the way for such superior rule rather than add an imprecise test of remoteness to the existing Hadley rule. The paper contrasts the English and US solutions with functional equivalents of remoteness from Germany, France and Quebec, which come closer than the common law to the economic models’ version of remoteness or the expectation damages rule. The analysis shows, perhaps surprisingly, that the efficiency of the common law of contract is too often taken for granted.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".