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Record W4256174237 · doi:10.3138/utlj.60.2.643

THE EVOLUTION OF CONTRACT REMEDIES (AND WHY DO CONTRACTS PROFESSORS TEACH REMEDIES FIRST?)

2010· article· en· W4256174237 on OpenAlexvenueno aff
George G. Triantis

Bibliographic record

VenueUniversity of Toronto Law Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsDamagesCompensation (psychology)IncentiveNormativeBreach of contractContract theoryLaw and economicsBusinessEconomicsLawPolitical scienceMicroeconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

This essay traces the evolution of the scholarly understanding of contract remedies, beginning with the era in which the compensation principle and expectation damages dominated. Fuller and Perdue's classic articles in 1936/1937 and the theory of efficient breach in the 1970s each offered important justifications for the principle. However, as economic analysis extended to incorporate a wider range of incentive and risk-bearing goals, support for the compensation principle became increasingly frayed. The emergence of the incomplete contracts theory further weakened its normative significance. In practice, the cutting-edge uses of contract damages pursue several other objectives unrelated to compensation. In particular, damages promote contracting goals by (a) providing prices for embedded options or (b) setting the stakes, and thereby incentives, for future litigation. Finally, the essay discusses the design of ‘tiered’ damages within a single contract that are triggered by different contingencies. In this sense, damages and contract conditions act as complements as well as substitutes. In light of the complex and interactive role played by contract remedies, the author suggests that this topic should be taught near the end rather than at the beginning of the first-year contracts course.

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)0.001
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 score0.956
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.254
Teacher spread0.243 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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