Bibliographic record
Abstract
The article considers the apparent tension between contract’s strict liability doctrine with respect to performance and the general moral precepts that liability should track fault and that one should internalize the costs of one’s own choices, but not the costs of arbitrary misfortune. By making contractors strictly liable for their failure to perform, the law attributes greater responsibility to agents than these moral principles seem to countenance – namely by according them legal responsibility for events or outcomes for which they bear no fault. Those precepts, however, are couched at a highly abstract level that is more appropriate for blame and punishment than for the sort of responsibility that contract law assigns. In the article, I defend the strict liability doctrine as a philosophically interesting default rule that supports trusting relationships between the parties and lays the groundwork for a healthier moral cooperative relationship between contracting parties than a fault-based system would. Strict liability norms relieve the promisee of pressures to supervise and intrude upon the promisor during performance and so eliminate some of the impetus to cultivate and display attitudes and behaviours of distrust. In turn, strict liability encourages the promisors to assume full responsibility for a project and by relieving promisees of pressures to intrude on the promisor gives the promisor a greater arena of autonomy in which to operate. While fault-based liability rules may encourage displays of distrust and sow the seeds of conflict, strict liability rules assign responsibility in ways that encourage trust and other components of healthy moral relationships. However, conceiving strict liability in this way brings out an internal tension between the justification of strict liability in contract and broad construals of the duty to mitigate, a doctrine that places the burden of self-help on disappointed promisees. As I will argue, broad construals of the duty to mitigate work at cross-purposes with the moral functions of a strict liability regime, offering further reasons to interpret the duty to mitigate narrowly.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".