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Record W2416877329 · doi:10.3138/utlj.3808

Enhancing moral relationships through strict liability

2016· article· en· W2416877329 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Toronto Law Journal · 2016
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsnot available
Fundersnot available
KeywordsDistrustLiabilityStrict liabilityDoctrineMoral responsibilityLaw and economicsDefault ruleBlameBusinessLawPolitical scienceSociologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.047
GPT teacher head0.224
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it