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Record W1590417720

The Problem with Pure Economic Loss

2009· article· en· W1590417720 on OpenAlexaff
Peter Benson

Bibliographic record

VenueScholar Commons (University of South Carolina) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTortDoctrineDutyDenialLaw and economicsNothingSet (abstract data type)AutonomyVariety (cybernetics)EconomicsLawLiabilityPolitical sciencePsychologyEpistemologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

It is now virtually a dogma among contemporary tort scholars that the non-recovery of pure economic loss in a variety of situations may be justified, if at all, only as a special, policy-driven rule that limits the usual operation of general negligence principles, in particular the foreseeability doctrine. This familiar view in turn rests on a further assumption as to the underlying conception of negligence in which the central concept is the standard of care, with the notion of duty being a matter of foreseeability and playing at most a subsidiary and instrumental role. My aim has been to challenge these views through a systematic reconsideration of the rationale for the denial of so-called relational economic loss claims – the longest standing category of non-recoverable pure economic loss. Drawing on the leading cases, I argue that the prevailing policy-driven explanation not only fails to account for core instances of the law but in fact obscures the basis for non-recovery as set out in the decisions. A completely different rationale for non-recovery emerges: one that is rights-based and that has nothing to do with foreseeability, limitations on foreseeability or the usual policy concerns which writers endorse and dispute. Not only does this rationale account for this area of negligence; it also suggests a larger conception of negligence which affirms the autonomy and priority of duty and is both rights-based and relational, in contrast to the dominant model widely assumed by scholars. I argue that it is this alternative conception that not only underlies the law’s treatment of relational economic loss but shows this to be fully consonant with the very same principles of negligence that govern physical 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 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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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